4KAgent: Agentic Any Image to 4K Super-Resolution
Authors: Yushen Zuo
Qi Zheng
Mingyang Wu
Xinrui Jiang
Renjie Li
Jian Wang
Yide Zhang
Gengchen Mai
Lihong Wang
James Zou
Xiaoyu Wang
Ming-Hsuan Yang
Zhengzhong Tu
Venue: NeurIPS 2025 poster ()
Keywords: Super-Resolution
Agent
Image Restoration
Low-level Vision
Primary area: applications
TL;DR: We present the first agent system for super-resolution that is capable of upscaling any image of arbitrary degradation to high-quality 4K resolution.

Abstract: We present 4KAgent, a unified agentic super-resolution generalist system designed to universally upscale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs at $256\times 256$, into crystal-clear, photorealistic 4K outputs. 4KAgent comprises three core components: (1) Profiling, a module that customizes the 4KAgent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision-language models alongside image quality assessment experts to analyze the input image and make a tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive execution-reflection paradigm, guided by a quality-driven mixture-of-experts policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial details in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover natural images, portrait photos, AI-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demonstrating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing a novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We release all the code, models, and results at: https://4kagent.github.io.

4KAgent:
Agentic Any Image to 4K Super-Resolution
Yushen Zuo',, Qi Zheng"l, Mingyang Wu Xinrui Jiang"!, Renjie Li', Jian Wang", Yide Zhang', Gengchen Mai Lihong V. Wang", James Zou
Xiaoyu Wang', Ming-Hsuan Yang", Zhengzhong Tu
Texas A&M University "Stanford University Snap Inc. "CU Boulder
UT Austin Califfomia Institute of Technology "Topaz Labs
UCC Mereed
*Coresponning Author:
edu. 'Equal contributions. Project Website: 4kagent github. io
4K Upscaling
Old Photos
Al-Generated
Pet Portraits
Landscape
Satellite
Microscopy
Pathology
Fundoscopy
Ultrasound
X-Ray
Figure 1: We present 4KAgent, an agentic image super-resolution generalist designed to universally upscale any image 4K, regardless of input type, degradation level, domain. That is, 4KAgent effectively restores diverse imagery, spanning from natural scenes, severely degraded captures (e.g., old photos), human/pet portraits, Al-generated content (AIGC), well as specialized scientific imaging domains, such as remote sensing. fluorescence microscopy, pathology, and various medical modalities like X-ray, ultrasound, and funduscopy- all without the need for any re-training or domain-specific adaptation.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Abstract
We present 4KAgent, a unified agentic super-resolution generalist system designed universally upseale any image to 4K resolution (and even higher, if applied iteratively). Our system can transform images from extremely low resolutions with severe degradations, for example, highly distorted inputs 256 256, into crystal-clear, photorealistic 4K outputs. Agnt comprises three core components: (1) Profiling, module that customizes the 4K Agent pipeline based on bespoke use cases; (2) A Perception Agent, which leverages vision- Laigu-la models alongside image quality assessment experts analyze the input image and make tailored restoration plan; and (3) A Restoration Agent, which executes the plan, following a recursive ceccution-reflectiin paradigm, guided by a quality-driven mixture-of- experts policy to select the optimal output for each step. Additionally, 4KAgent embeds a specialized face restoration pipeline, significantly enhancing facial de- tails in portrait and selfie photos. We rigorously evaluate our 4KAgent across 11 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging domains. Our evaluations cover nat- ural images, portrait photos, Al Al-generated content, satellite imagery, fluorescence microscopy, and medical imaging like fundoscopy, ultrasound, and X-ray, demon- strating superior performance in terms of both perceptual (e.g., NIQE, MUSIQ) and fidelity (e.g., PSNR) metrics. By establishing novel agentic paradigm for low-level vision tasks, we aim to catalyze broader interest and innovation within vision-centric autonomous agents across diverse research communities. We release all the code, models, and results at: https: : / 44kggntt....../ github. ic.
Introduction
Image super-resolution (SR) is fundamental task in computer vision that aims to reconstruct high- resolution (HR) images from their low-resolution (LR) counterparts [232, 45, 46, 98, 267, 202, 201, 168. 195]. serves as bedrock for various low-level vision tasks [257, 119, 246, 186]. including deblurring [182, 33], dehazing [63, 106], deraining [165, 79], and low-light enhancement [211, 59]. Beyond its classical role in computational photography and imaging, SR techniques significantly influence numerous domains, such as biomedical imaging [55, 169], remote sensing [171, 64, 92], surveillance [259], and embodied artificial intelligence applications [60, 170, 75].
Traditional SR methods [45, 202] typically assume known synthetic degradation during training, which limits their generalization real-world captures that suffer from complex, heterogeneous, and unpredictable degradations [201]. Recent research has increasingly shifted to a more practical real-world super-resolution (RealSR) task [21. 216]. which attempts to explicitly address diverse and unknown degradations in naturally captured photographs and videos. RealSR requires models not only to handle multiple combined degradations effectively but also exhibit strong adaptability and generalization across varied scenarios [238, 187]. Many effective solutions have been proposed to solve the RealSR problem, via simulating complex real-world degradations [256, 201], leveraging the powerful generative prior of pre- trained diffusion models [81, 216, 217, 179, 144], enabling robust restoration under unknown conditions. Inspired by the advanced planning and reasoning capabilities
of large language models (LLMs) [213, 71, 237, 48], agentic restoration frameworks [22, 274] have emerged as an advanced tool that can adaptively handle multiple degradations through sequential planning and dynamic restoration strategies.
Despite their successes in certain scenarios, existing performant generative approaches [216, 179] can only handle limited degradation ranges, e.g., up to 4x upscaling, failing to recover extremely low-quality images with highly complex and diverse degradations the wild. Moreover, SR specialist models are known to generalize poorly to out-of -itt-----ittiuttion domains [23], let alone when applied to a different scaling factor. This is mainly due to heavy reliance on supervised learning on synthetic image pairs that cannot fully capture the complex real-world image degradations, not to mention other domains, ranging from Al-generated imagery, scientifie computing, to biomedical images. Last but not least, practically, users often demand highly specific workflows, e.g., either denoising, upscaling, or prioritizing high fidelity over perceptual quality, and hence one-size-fits-al system that can flexibly adapt to satisfy diverse user needs and application scenarios is in pressing need.
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To fill this gap, we present 4KAgent, the first-of -tst-of-its-eee kind agentic framework for generic, flexible, and interpretable super-resolution of any image 4K. As illustrated in Fig. 1, 4KAgent capable of upscaling any low-resolution image (e.g., 0.065 megapixels) to 4Kx4K, (i.e., 16.7 megapixels) via 16x upscaling factor' ($3.4). It also sets new state-of-the-arr (SoTA) on classical image super-resolution ($3.1), real-world image super-resolution ($3.2), face restoration (Appendix), and mulliple-degaadttoon image restoration ($3.3) benchmarks, in terms of perceptual quality. We also show that 4K.Agent generalizes across widespread applications in low-level tasks, such as joint restoration & 4K upscaling ($3.5), and Al-generated content 4K upscaling ($3.6). Lastly, thanks to the mixture-of -oree--f-exeppt---ee and profile design, 4KAgent demonstrates broader impacts interdisciplinary areas such as scientific super- super-resolution ($3.7), including O Satellite image super-resolution @ fluorescence microscopy super-resolution, and @ medical image super-resolution.
Our contributions are as follows:
Framework] We present Agent, the first Al agent framework for universal any-image-to-4K upscaling, capable of handling all image categories, ranging from classical and realistic degraded and extremely low-quality images to Al-generated imagery and scientific imaging domains such as remote sensing, microscopy, and biomedical imaging.
ISystem Design] We design a multi-agent system in 4KAgent. The Perception Agent employs large vision-languee
models (VLMs) to analyze the content and distortion in the image and provide the restoration plan for the restoration agent to execute. The Restoration Agent, which sets up an execution-reflectte reflection-rolllest procedure for recursive restoration and upscaling.
[Q-MoE & Face Restoration pipeline] In each restoration step of the restoration plan, we propose fixture-of--xper (Q-MoE) policy in execution and reflection select the optimal image. We further develop a face restoration pipeline to enhance faces in images.
Quality-Driven
[Profile Module] To expand the applicability of 4K Ageett, we propose a Profile Module to bring the availability to customize the system for different restoration tasks. 4KAgent can adapt to different restoration tasks without extra training.
[DIV4K-50 Dataset] To evaluate 4K super-resolution performances, we build the DIV4K-50 dataset as a challenging test set to upscale a low-quality (LQ) image in 256 x 256 resolution with multiple degradations to high-quality (HQ) 4K image in 4096 x 4096 resolution.
[Experiments] Extensive experimental results demonstrate the superiority of 4KAgent as a gen- eralist 4K upscaling agent: 4KAgent sets new state-of-the-an on variety of real-world image super-resolution benchmarks, multiple-degradation restoration benchmarks, face restoration, 4K upscaling task, and various scientific imaging tasks, including satellite image super-resolution, fluorescence microscopic imaging, X-ray radiography, and biomedical imaging super-resolution.
2
Method
2.1 System Overview
We introduce 4KAgent, a multi-agent framework designed upscale arbitrary images to 4K res- olution. Fig. 2 illustrates the overall workflow of our proposed 4KAgent, which decomposes the restoration pipeline into collection of specialized agents. The Perception Agent analyzes degra- dations (noise, blur, etc.), extracts semantic and structural cues, and schedules a restoration plan containing a sequence of operators (denoising, deblurring, super-resolution, etc.). The Restoration Agent follows the restoration plan using proposed -uriie Mixture-of-Expert (Q-MoE) to select the best output from multiple restoration tools. The rollback mechanism is activated if the qual- ity of the restored image falls below a threshold. Additionally, a dedicated Face Restoration Pipeline further enhances facial regions by triggering expert face restoration models. user-configurable Profile Module allows users to customize the system (e.g., prioritize fidelity perceptual quality), enabling robust, high-quality 4K SR across diverse content and degradation types.
2.2 Perception Agent
The Perception Agent is designed as four-stage analytical module that bridges low-level image quality assessment with high-level reasoning. Its core function is to extract a robust and holistic
4Kggntt can actually achieve large-scale super-resolution (e.g., 32x, 6... applied recursively [168].
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Profile Module
Perception Agent
Restoration Agent
Task
38
Pace
Stage Image Analyror
Execution
Rollback
Proference
Profiie
Quality
Reflection
Stage Degrodation
Face Restoration
lask Planning
Descriptio
Tack 2
Restoration
Tisk K
Res: 4144x3104
Stago
CLEPIOAN: EABA
Task Super-Resslution
Denoising
Seper-Resolttion
TOrro:
MUSIQ:
K2.022
Task Super-Resolution
Expert IQA
NIgE:
3.8GG
Quality Scores
Execution
G-MoE
Face Crop
Restaration
Stage
@. Deseription
Experience
Tool
Tool 2
Tool N
Tool
Tool
Tool
brows loit, fouriigiagg offins."
Agenda Distortions
VLM
VLM
Distoction
Desectpsion
Srown-bared Sedection for finc
Qualrty Scores
"aaua
Reflection
Scove-lused Selection
Fooe O-MoE
Upecaling Setting
Paee
High quilt??
Super- Ruper-Resolttion
Restoration Plan
Denoising
Super- Resolution
Rollbock
(0.927,
Oulpul
Figure 2: 4KAgent system overview.
understanding of the input image in terms of both semantic content and low-level degradations, and to create restoration plan that guides the subsequent restoration process.
Image Analyzer. Perception agent invokes suite of expert Image Quality Assessment (IQA) tools that evaluate the input image I across multiple quality dimensions Qr (Q1, Q2...... Specifically, we adopt CLIPIQA [194]. TOPIQ [20]. MUSIQ [83], and NIQE [260] as the IQA metrics. These metrics represent perceptual quality from diverse aspects (due to their different model designs and training data), which are employed as context for the next step of degradation reasoning.
Degradation Reasoning. Perception agent leverages a VLM MR to reason over the obtained IQA metrics. Specifically, by incorporating the input image I, IQA metries Q₁, the VLM Ma predicts the degradation list D, from the input image, which corresponds to an initial restoration agenda Ay. Meanwhile, Mn also analyzes the content in the image and outputs the corresponding image descriptions C₁ (i.e,, captioning). The whole process can be expressed as C₁, Dj, A₁ M Mn(T, Qr).
Upscaling Factor Configuration. 4KAgent is able automatically determine and apply an appropri- ate super-resolution scale to reach 4K. Given an input image I with height H, and width W,, the scale factor is calculated by 8 min
E (2, 4, 16) Imax(H, W7) 40003 U (16)): After obtaining the initialized agenda A', from Mp. 4KAgent calculates the scale factor and appends super-resolution task(s) into A' to obtain the final agenda A,. Under this setting, 4KAgent is able to upscale any image (resolution larger than 250 x 250) to 4K resolution in single process.
Task Planning. After obtaining the degradation list D2 present the input image and the restoration agenda A,, the perception agent employs LLM VLM Mp provide the restoration plan. Specifically, by incorporating image descriptions C, degradation list D,, restoration experience E, and input image itself / (available when using VLM Mp), Mp outputs initial restoration plan P. = (C, Dr, Ar, E, I), which contains a sequence of restoration tasks.
2.3
Restoration Agent
Building upon the task plan P, provided by the Perception Agent, the Restoration Agent executes an iterative process, each stage which tightly couples restoration and evaluation using an execution- reflection-rollbace. triplet. Within this agent, propose Quality-Drived Driven Mixture-of of-peerss- (Q-MoE) policy, both execution and reflection, to select the optimal image for each restoration step. We also employ rollback mechanism to adjust the restoration plan if necessary.
Execution. Guided by the task plan Pr, this agent executes the restoration step by step. In each restoration step, the input image goes through all tools in the toolbox. which contains number of

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advanced restoration models (detailed in the Appendix) corresponding to each individual restoration task. In 4KAgent, we curate different restoration tasks that are useful to enhance image quality: Brightening, Defocus Deblurring, Motion Deblurring, Dehazing, Denoising, Deraining, JPEG Compression Artifact Removal, Super Resolution, and Face Restoration. Specifically, for step in the restoration plan, it produces multiple restoration results (T=-)= = 1 ~ N) (T; is i-th tool in the toolbox, N is the number of tools in the toolbox) based on the input image 45-1-
Reflection. After obtaining restoration results {1)(4 = ~ N,, restoration agent selects the best image based on their quality. To evaluate the quality of image Ti( we compute the image quality score by combining the preference model HPSv2 [219] and no-reference IQA metrics. Specifically, we use HPSv2 to assess the human preference of the resulting image T, (fx-1) based on the image content description C,. For no-reference IQA metrics, we employ NIQE [148], MANIQA [233], MUSIQ [83], and CLIPIQA [194] to calculate a weighted sum as its no-reference quality ((T;(T&-1).CC
+ Qu (TT((T=1))/ 4, where Qnr (T: = WNIQE (1 - QNIQE/ + Ejen Wy 2j. 2 (MUSIQ, MANIQA, CLIPIQA), H indicates the HPSv2 evaluation. After obtaining the quality score of each result image, the final result of this restoration step is obtained by the highest quality score: 4 = T-- (Ix-1), '" = arg max.g(1....N)) (T,(())) The combination of execution and reflection can be viewed as a Mixture-of-Expers (MoE) system, which we refer to Quality-Driven Mixture-of-Fxxprtt (Q-MoE): the input image is processed through each expert (execution), and the Reflection function selects the optimal image among all.
score: (T)(
Rollback. Following previous AI Agent systems [160, 274, 268, 113, 69], we also design rollback mechanism in the 4KAgent system. Specifically, if the quality score of L& lower than a threshold 7, i.e., Qa S 17, the restoration step is seen as failure step, and 4KAgent generates a failure message Sy. Then the system rolls back to the input image Iς-1 and assigns a different restoration task in this step. (Details are provided in the Appendix)
2.4 Face Restoration Pipeline
Human face regions are often the most visually sensitive and semantically important components in an image. However, conventional super-resolution methods struggle to maintain identity consistency, natural skin textures, and perceptual quality when restoring faces, especially in heavily degraded portraits. To address this, 4KAgent incorporates a dedicated Face Restoration Pipeline, which is selectively triggered within the restoration workflow. The Face Restoration Pipeline is embedded as a submodule 4KAgent and only invoked after the super-resolution restoration step, ensuring that face quality refinement is seamlessly integrated into the iterative restoration loop.
The overall framework of the Face Restoration Pipeline in 4KAgent is shown in Fig. 3. First, 4KAgent detects and crops faces in the input image {F),/ = 1 ~ L} (L is the number of faces in the image I). Then, if super-resolution is in the restoration plan and the resulting image 4 of ruser-resolution step does not trigger the
Crop
Restoration Pipeline
Ciddftenee
PONMAN
Face Quality-Diveen Mol
Super Revolation
Bos Face
rollback mechanism, 4KAgent will detect and crop faces the resulting image (F%
1 L'} (L' is the number of faces in the image It). If L L', then for each face in Is. different advanced face restoration methods are applied, yielding restored faces {T: (F), ), = ~ N Here, T. is a face restoration tool in the toolbox, TV is identical function, and N is the number of face restoration tools in the toolbox. Likewise, we also apply the Q-MoE policy here: 4KAgent selects the best face based on the quality score Q!. The quality score Q! not only considers face quality, but also identity preservation:
Crop
Paste Back
Input Image
Output Image
Figure 3: Face Restoration Pipeline overview.
(T! (F1.) = wip IP(TT (F), F/) + WIQn (Qn (T! (F) ))44 + QCr(T) (FI))) (1)
where / = 1 ~ L. IP calculates the cosine similarity of face features, extracted using ArcFace [42]. CF indicates CLIB-FIQA [156], which is an advanced face IQA metric. 4KAgent combines the no-reference quality score used in the reflection stage and the CLIB-FIQA score to assess the face quality. After obtaining quality score Q! 4KAgent selects the best face Flug: F (F),),
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arg m (o, (T/ (F).) 4KAgent will paste Fout back to the original image Ix. then proceeding to the next step.
2.5
Profile Module
To enhance the flexibility and applicability of our 4KAgent system, develop the Profile Module, enabling dynamic customization for diverse image restoration scenarios, according to user require- ments. Specifically, the Profile Module serves role analogous to customization module in LLM applications, allowing fine-grained control through the following seven configuration parameters:
1. Perception Agent: Specifies the choice of LLM/ / VLM employed by the Perception Agent.
[Default: L1 alaamaisioon
2. Upscale 4K: Determines whether upscale to 4K resolution. [Default: True]
3. Scale Factor: Explicitly defines the upscale factor for the entire pipeline. (Default: 4,
Optionss [2, 4, 8, 1 6]). This parameter overrides "Upscale 4K" when specified.
4. Restore Option: Explicitly sets the restoration task(s) to be applied. If set to None, restoration task(s) are determined automatically by the Perception Agent. (Default: None)
5. Face Restore: Toggles activation of the dedicated face restore pipeline. (Default: True) Brightening: Controls the activation of image brightening, which may cause color shifts in restored images. Provided [Optional] to maintain image color fidelity. (Default: False) ke) Restore Preference: Defines whether to prioritize higher perceptual quality or higher fidelity in image restoration. (Options) [Percepti on, Fide Fidelityl. Default Percept ion). Here we respect the perception distortion tradeoff [14, 270], deeming models that optimize for distortion metries (e.g., PSNR, SSIM (208)) F Fidell 1 ty models while methods trained for perceptual quality (e.g., NIQE [ [148], MUSIQ [83]) as Percepti on models.
cerception-distortion
The Profile Module offers exceptional configurability, enabling seamless adaptation to wide range of restoration tasks without requiring model retraining or domain-specific fine-tuning. To the best of our knowledge, 4KAgent is first-of-its-knnd framework that enjoys unprecedented robustness and generalizability: each distinct restoration scenario can be addressed by simply selecting an appropriate configuration profile, thanks to which 4KAgent consistently achieves excellent performance across variety of challenging restoration domains. Comprehensive details on predefined profiles in 4KAgent and their naming conventions are further elaborated in the Appendix.
Experiments
We evaluate 4KAgent wide range of 11 image super-resolution tasks, including classical image SR (4x) ($3.1), real-world image SR (4x) ($3.2), multiple-degradation image restoration ($3.3), face restoration (4x) (Appendix). large scale factor SR (16x) ($3.4), joint restoration with 4K upscaling ($3.5), and Al-generated content 4K SR ($3.6), remote sensing image SR (Appendix), fluorescence microscopy image SR (Appendix), and medical image SR (Appendix). In total, we evaluate 4KAgent on 26 benchmarks. The summary of datasets used in experiments is shown in the Appendix. We also present the parameter setting, the details of the toolbox, and prompts used in the LLM / VLM component of 4KAgent in the Appendix.
3.1 Classical Image Huper-Resolure
In this section, we follow the classi-
Table 1: Quantitative comparison on classical image SR benchmark (B100). The best and second-best results are marked in bold and underline.
cal SR experiment setting [266, 119]
and evaluate 4KAgent on classical SR benchmarks, including Set5 [13], S [249], B 100 [140], Urban 100 [70], and Mangal 109 [ [142]. In addition to PSNR and SSIM [208], we evaluate images
|SNRT
SSIM;
LPIPS).
FID:
NIQEJ
MUSIQU
SwinlR [119]
27.92
0.7489
0.3548
94.57
6.27
57.71
X-Restomer [28]
21.99
0.7508
0.3521
90.52
6.21
57.91
HAT-L
with LPIPS [262], FID [66], NIQE [148], and MUSIQ [83] for more com- prehensive evaluation.
DitBIR [123]
24.99
0.6156
0.2719
84.99
3.92
68.23
OSEDiff (216]
24.35
0.6495
0.2408
73,23
4.08
68.54
AgensicIR [274]
22.51
0.5853
0.3078
102.99 02.992
4.06
68.36
Alen (FxpSR-s4-F))
28,09
(L7540
0.345%
88.39
6.02
$9,12
4KAAgent (4xpP)-s4.PP)
0.2387
23.64
3.86
Due to the high flexibility AKgget, which is governed by configurable pro-
4KAAgen (GenSR-s4-P))
23.64
0.6246
0.2572
78.50
3.93
69.44

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files, we use three different profiles to customize the 4KAgent in this experiment: ExpSR-s4-F ExpSR-s4-P, and GenSR-s4-P. For comparison, we use state-of-the-art fidelity-based methods (e.g., SwinIR [119], X-Restormer [28], HAT [29]) and perception-based methods (e-g.. DiffBIR [123], OSEDiff [216]). We also include AgenticIR [274] for agentic system-level comparison. We present experimental results on the B B100 dataset in Tab. 1. Detailed experimental results and visual comparisons are shown the Appendix. Based on quantitative comparisons. 4KAgent outperforms AgenticIR across nearly all evaluation metrics classical SR benchmarks. Moreover, AKAgent can easily output images with high perceptual quality or high fidelity by setting different profiles.
3.2 Real- World Image Super-Resolution
In this section, we follow previous Real- ISR methods and evaluate AKggent on real-world image super-resolution datasets (RealSR [17], DrealSR [214]).
Table 2: Quantitative comparison on Real-Worihe image SR benchmark (RealSR). The best and second- bentt results are
marked in bold and underline.
We use two different profiles to cus- tomize 4Keget in this experiment: ExpSR-s4-P and GenSR-s4-P. We compare 4KAgent with state-of-ther of-the-art methods, including ResShift [245], Sta- bleSR [195], DifIBIR [123], PASD [234], SceSR [217], SinSR [205], and OSEDiff [216]. We also employ Agen- tielR in this experiment for agentic sys- tem comparison. We adopt the same evaluation metrics as in the classical SR experiments. Experi- mental results on the RealSR dataset are shown in Tab. 2. 4Kggent outperforms AgenticIR in every metric regardless of profile setting. Moreover, 4KAgent sets a new state-of-the-an performance on no-reference perceptual metrics (e.g., NIQE, MUSIQ). Detailed experimental results both datasets and visual comparisons are shown in the Appendix.
Method
PSNR1
SSIM:
LPIPS
FID.
NIQE,
MUSIQU
ResShit [245]
26.31
0.7411
0.3489
142.81
7.27
58,10
StableRR [195]
DiMBOR [123]
24.88
0.6673
0.3567
124.56
5.63
64.66
PASD [234]
25.22
0.6809
0.3392
123,08
5.18
68.74
SoeSR [217]
25.33
0.7273
0.2985
125 66
5.38
69.37
SinSk [205]
26.30
(1.3554
0.3212
137.05
6.31
60.41
OSED [216]
123.50
AgensicIR (274]
22.45
0.6447
0.3745
140.38
5,81
65.87
4Kent (ExpSR-s4.P)
24.60
0.6839
0.3253
5.09
70.97
4KAgent (GenSR-s4.P)
22.55
0.6557
0.3509
134.63
4.78
71.77
3.3 Multiple-Degradattion Image Restoration
In this section, follow the setting of AgenticIR, using the Group A, B, and C test sets, which contains 1,440 LQ images processed with 16 combinations of degradations ap- plied to images from the MiO100 dataset [91]. In this experiment, we configure 4KAgent with GenMIR- profile. We compare 4KAgent with several all-in-one models: Air- Net [107], PromptlR [162], MiOIR
Table 3: Quantitative comparison of multiple-degradtttion image restoration tasks the Group C subset. The best and second-bee results are marked in bold and underline.
Method
PSNR1
SSIM:
LPIPS)
MANIQAT
CLIPIQA1 MUSIQ"
AirNer [107]
17.95
0.5145
0.5782
0.1854
0.3113
30.12
PrompiR [162]
18.51
0.5166
0.5756
0.1906
0.3104
29.71
MIOIR [X0]
15.63
0.4896
015376
0.1717
0.2891
37.95
[136]
InstruedR [36]
17.09
0.5135
0.5582
0.1732
0.2537
33.69
AutoDIR
18.61
0.5443
0.5019
0.20445
0.2939
37.86
AgentiIR [274]
18.82
0.5474
0.4493
0,2698
0.3948
48.68
MAIR [S0]
19.12
0.5514
0,4142
0.2798
0.4239
$1.36
[90], DA-CLIP [136], InstructIR
4KAgent (GeMMIR-P))
19.77
0.5629
0.4271
0.5233
55.56
[36], AutoDIR [8 ], and agentic sys-
tems: AgenticIR [274] and MAIR [80]. Experimental results are shown in Tab. 3 and Fig. 4.
LD
Figure 4: Visual comparisons on the Mi0100 dataset. (Please zoom in to see details.)
We present experimental results Group C here. Experimental results for all groups are shown in the Appendix. As summarized in Tab. 3, 4KAgent achieves state-of-the-art performance on all metrics excluding LPIPS. Fig. 4 shows that 4KAgent generates images with finer-grained details, which are
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more consistent with the HQ images (e.g., the hand and bottle on the left and the skin of the lizard on the right). These results demonstrate the superiority of 4KAgent under complex degradations.
3.4 Large Scale Factor (16x) Image Super-Resolution
In this section, we target a challenging setting, 16x real-world image upscaling. For the dataset used in this experiment, select RealSRSet [256], real-world dataset consisting of 20 low-quality images for large-scale
experiment. Specifically, we configure 4KAgent with the Gen4K-P profile for this experiment. Based on the resolution of images in the dataset, 4KAgent will upscale each image with scale factor of 16.
We compare 4K Agent against HAT-L [29], DifIBIR [123] under different settings: (1) 4x 4x: first upscale the low-quality image by a scale factor of 4, then upscale the upscaled images obtain the 16x upscaled image. (2) 16x: directly upscale the low-quality image by scale factor of 16. We also extend AgenticIR for large scale factor (16x) image super-resolution for agentic system comparison. Visual comparisons of different methods are shown in Fig. 5. 4KAgent generates fine-grained and realistic details that are more visually pleasant than the comparing methods (e.g., the rock and grass textures). Quantitative results and more visual comparisons shown in the Appendix.
HAT-L 4x)
(16x)
AgentieIR
4KAgent (Gen4K-P))
Figure 5: Visual comparisons on the RealSRSet dataset. (Please zoom in to see details)
3.5 Joint Restoration & 4K Upscaling
In this section, we bring Agent to the most challenging setting: Joint multiple-degradation image restoration and 4K upscaling. As there are no previous methods and datasets targeted at this setting, construct
evaluation dataset, DIV4K-50, based the Aesthetic-4K dataset [254] to rigorously test end-to-end restoration and ultra-bigh-scale SR. Specifically, we select 50 images from the Aesthetic-4K dataset based its content, then center-crop each image 4096 4096 as the high-quality (HQ) ground truth image. We downsample HQ images to 256 256 and randomly apply combinations of defocus blur, motion blur, additive Gaussian noise, and JPEG compression to generate the corresponding low-quality (LQQ images. In this experiment, we also configure 4KAgent with the Gen4K-P profile. We compare 4KAgent with more methods in this experiment (OSEDiff [216), PiSA-SR (179]) with more upscaling settings.
As shown in Fig. 6, 4KAgent reconstructs finer and more natural details than the comparing methods (e.g., the facial features). Quantitative results and more visual comparisons are shown in the Appendix.
LQ
H4X- 4x)
DaMBRR (4x 4x)
OSEDif (4x - 4x)
PSSA-SR (4x - 4x)
Agentie1R
4KAgent (Gen4K-P)
HQ
Figure 6: Visual comparisons on the DIV4K-50 dataset (Please zoom in details).

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3.6 Al-Generated Content (AIGC) 4K Super-Resolution
In this section, we investigate super-resolution in AIGC scenarios by comparing direct 4K genera- tion with IK followed by upscaling using AK gent. As prior method and dataset targets this setting, we sample 200 prompts from two stan- dard AIGC benchmarks [210, 103] and generate both IK and native 4K images using several gen- erative models [95, 49, 24, 229, 72, 254], which we denote as GenAIBench-4K and DiffusionDB- 4K. We use the ExpSR-s4-1 profile 4KAgent in this experiment. To better capture local degra- dations 4K images, we introduce MUSIQ-P, patch-based metric that averages MUSIQ scores over non-overlapping x12x512 regions.
Table 4: Quantitative comparison of AIGC 4x SR in GenAlBench-44K [103]. Bold denotes top performers: underlines indicate second and third. MUSIQ-P" is patch-applied MUSIQ metric for evaluating 4K images.
Model
NIQE, MANTQA? MUSIQ-P"1 CLIPIQA
SANA-IR [229)
4.18
66.30
+4KAgent
3,03
0.4735
57.97
0,7050
GPT4o [72]
5.69
0.4997
64.43
0.6607
+ +gK.ggent
3.56
58,28
0.7016
FLUUX. F-dev [95]
6.18
0.5018
61.02
0.6768
+ 4KKAgem
2.98
0.5034
58.19
0.7078
PixAst-E [24]
4.12
0,4415
63.74
0.6960
Agent
2.76
0,4699
56.71
0.7077
SD3-Medium [49]
5.03
0.4767
64.68
0.6922
+ +gKAgent
2.99
0.5155
60.22
0.7169
Fig. 7 presents models enhanced by 4KAgent produce finer-grained details compared to native 4K generation methods [254, 229]. As shown Tab, 4, 4KAgent consistently boosts both perceptual quality and semantic alignment on GenAlBench-4K, surpassing their original IK generation baselines. In addition, we observe that AKegtt demonstrates stronger alignment with human preferences compared to native 4K generation methods, as evidenced by higher PickScore [88]. Additional comparisons with native 4K methods and results on DiffusionDB--44 are presented in the Appendix.
Diffusion44K
(P 4K.
Diffusion-44 4K
Flux 4KAgent
An action photograph of cat casting fur out if it's mouth.
A magician pulling a rabbit out of hat.
SANA-
PixA Sugma 4K.4geir
SANA 4KAgeez
Figure 7: Qualitative comparison between native 4K image generation and IK image generation methods with 4Kggent, using identical prompts.
3.7 Scientific Images
In this section, extend the evaluation of 4KAgent across broad spectrum of cross-domain scientific image super-resolution applications, where high spatial fidelity crucial but often limited by sensor cost and physical constraints [184, 185, 188]. The explored imaging domains and corre- sponding benchmark datasets are as follows: For remote sensing image super-resolution, we evaluate on four benchmark datasets covering varied land-use patterns and sensing characteristics, including AID [223], DIOR I]. DOTA [222], and WorldStrat [37]. For fluorescence microscopy super- resolution, we compare the performance of 4KAgent with set of representative deep learning-basee based single-image SR (SISR) methods on SR-CACO-2 dataset [12]. For biomedical super-resolution, datasets from 4 distinct modalities are considered: beSR [77] in pathology microscopy, Chest X-ray 2017 [85] and Chest X-ray 14 [199] in X-ray, US-Case [175] and MMUSIK [154) in ultrasound, and DRIVE retinal vessel dataset [176] in fundoscopy.
Visual comparisons are shown Fig. 8, showcasing six imaging modalities side by side: remote sensing, fundoscopy, confocal fluorescence microscopy, pathology, X-ray, and ultrasound from left to

---

Brtwakoupp
Figure 8: Agent in processing scientific images. (Low-quality input vs. 4K result)
right. The top and bottom rows display the LQ inputs and 4KAgent outputs, respectively. Detailed quantitative and qualitative results are presented in the Appendix.
4 Related Work
We briefly review related works in three key areas. In image super-resolution, early CNN- based methods such as SRCNN [45] laid the foundation, followed by architectural innovations such residual and dense connections [87, 121, 267], attention mechanisms [19, 41, 266], and generative models including GAN-based [98, 201, 256, 21, 118, 119, 105] and diffusion- based methods [195, 234, 123, 217, 168, 205, 216]. In image restoration, progress has been driven by residual learning [255, 131, 257], attention modules [242, 25, 186, 57, 247], transform- [189, 275, 246, 206], well as GAN-based [58, 11, 56, 73, 147, 158] and diffusion-based models [123, 221, 81, 204, 82, 50], with recent efforts addressing multi-degradation scenarios via unified models [107, 138, 159, 236, 252]. In LLM agents, foundational reasoning frameworks like CoT [2]2], ReAct [237], and CoALA [178] have inspired domain-specnt systems such as MMC- TAgent [93]. VideoAgent [198], and MedCoT [129]. Notably, RestoreAgent [22], AgenticIR [274],
MAIR [80]. HybridAgent [104], and Q-Agent [272] demonstrate how LLMs can orchestrate multi- step visual restoration workflows via perception, planning, and quality-aware decision-makingg For full discussion, please refer to the Appendix.
5 Concluding Remarks
In this paper, we introduce 4KAgent, a versatile agentic image super-resolution generalist model designed universally upscale images of diverse types and degradation levels 4K resolution. By leveraging advanced multi-expert integration, adaptive decision-makee and specialized tools for perception and fidelity optimization, 4KAgent significantly enhances restoration quality across various challenging domains, including severely degraded images, natural scenes, portraits, AI- generated content, and specialized scientific modalities such remote sensing, microscopy, and medical imaging. Extensive evaluations on both standard benchmarks and specialized datasets demonstrate that 4KAgent consistently outperforms existing state-of- state-of-the-art approaches, especially in complex scenarios where conventional super-resolution methods fall short. This robust performance, achieved without domain-specific retraining, highlights the generalizability and practical utility of 4KAgent for generic deployment in both consumer, commercial, and scientific applications.
Future Work Looking ahead, we identify several promising directions to further enhance the capa- bilities and applicability of 4Kggent. First, we plan to improve system efficiency by developing more accurate distortion distortion--prcepttoon modeling and refining the rrecution-rellection for faster and higher-quality image restoration. Second, we will strengthen the safety and robustness of 4KAgent mitigate risks such as privacy leakage and harmful content generation. Finally, we aim to continuously expand the 4KAgent toolbox by incorporating additional domain-specific restoration methods and designing targeted profiles to better support diverse user needs and application scenarios.
mechanism
Acknowledgements
Gengchen Mai is supported by the NSF under Grant No. 2.21631.
10

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A
Model Card
A.1 Profile
4KAgent is highly flexible through configurable profiles. Users easily customize 4KAgent by selecting pre-defined profile or creating a new one. We provide a set of representative pre-defined profiles in Tab. 5, which cover most cases and include all the profiles used in our experiments. This design enables easy and intuitive customization for unseen scenarios.
Table 5: Pre-defined Profiles in 4KAgent.
Profiee Nickname
Perception Agent
Upscale to 4K
Scale Factor
Restore Option
Face Restore
Brightening Restore Preference
Gen4K-P
DepiciQA [241]
True
None
None
True
False
Perception
Gen4K-F
DepiciQA [241]
True
None
None
True
Fulse
Fidelity
Acr4K-P
Llama-3 2--iiinn [6]
True
None
None
False
False
Perception
Acr4K-F
Llama-3 3.2-iioo [6]
True
None
None
False
False
Fidelity
AerSR-s4-P
Llamu- 3.2. 2.siinn (6]
False
None
False
False
Perception
AerSR-s4-F
Llama-3 3.-iisnn [6]
False
None
False
False
Fidelity
ExpSR-st-P
Mlana-32-VVisin [6]
False
super-resolotion
False
False
Porception
ExpSR-s4-I
LIama-322
super-resolution
Fake
Fidelity
ExpSR-s2.F
super-resolution
Fidelity
ExpSR-s8-F
Llama-3.2-Vision
False
super-resolution
False
False
Fidelity
GenSR-st-P
DepictQA [241]
False
Nonc
Falsc
False
Perception
GenMIR-P
DepiciQA [241]
False
None
False
True
Perception
E4pSRFR.c4.P
Llama-3.2 3.2.iisionn
False
super-resolution
True
Falke
Perception
GenSRFR-s4-P
DepictQA [241]
False
None
True
False
Perception
We combine restoration type, restoration task, and restoration preference to construct the profile name. For example, Gen indicates a General image, Aer indicates Aerial image, 4K indicates "Upscale to 4K" on, and P indicates to restore the image with high Perceptual quality. Exp corresponds to Explicit setting, indicating that the profile has explicitly set the restoration MIR indicates Multiple Image Restoration. FR
Profile naming convention:
task (e.g.. SR, which indicates indicates Face Restoration. s4 indicates to upscale the image by a scale factor of 4.
4Agent supports various VLMs and LLMs within the Perception Agent, enabling effective analysis of image content and degradation. Specifically, users can select from DepictQA [241] or Llama- 2-Viio (11B) [6], while the system can be readily extended to incorporate more recent VLMs, egg. Qwen2.5-VL. [10]. For the VL.Ms or LLMs to schedule the restoration plan, users can choose from GPT-4 [4], or Allama-3.2-visionn
This is configured by the Perception Agent the profile module. For example, when it is set to -lama-3.2-Vision, the Llama-3.2. 2aama-3.2-Visin Vision model serves as the core engine to perceive image content and degradation, and then schedules the restoration plan Pr- As DepictQA is fine-tuned for image quality assessment (IQA), when it is set the VLM in the perception agent, AKgget employs Llama-3.2 3.2-iiiin to obtain the image description Cj and GPT-4 [4] to schedule the restoration plan.
A.2 Model Zoo
4KAgent constructs a restoration toolbox comprising nine distinct image restoration tasks: Bright- ening, @ Defocus Deblurring, 2 Motion Deblurring, Dehazing, ① Denoising, @ Deraining, ? JPEG Compression Artifact Removal (JPEG CAR), O Super Resolution, and @ Face Restoration. For each of these tasks, we integrate advanced state-of-the-ar methods into our comprehensive restoration toolbox. Detailed correspondences between restoration tasks and their representative methods are presented below, where 'QF' denotes the JPEG Quality Factor and 'BQF" indicates methods that are blind to the Quality Factor in the JPEG CAR task.
27

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Brightening
Defocus Deblurring
Dehazing
Super Resolution
DehuzefFormer [174]
DiABIR [1233]
CLAHE [276]
ConvIR [39]
DifiPlugin [132]
DRCT [68]
PourierDiff [137]
Constant Shilt (C=40)
DifPPlugin [132]
IFAN [100]
Dilfluugin [132]]
DRBNet [167]
X Restormer [28]
MAXIM [186]
RIDCP [218]
HAT-GAN [29]
HMA [34]
[29]
Gamma Correction
LaKDNet [166]
OSEDiT [216]
2/3) MAXIM [186]
Restormer [246]]
Deraining
PISA-SR [179]
SwinIR [119]
Denoising
Motion Deblurring
DifiPlugin [132]
MPRNet [248]
MAXIM [186]
X-Restormer [28]
SwinFIR [253]
SwinlR (Real (SR) [119]
[89]
Restormer [246]
MAXIM [186]
LaKDNet [166]
X-Restormer [28]
JPEG CAR
MPRNet [248]
MAXIM [186]
NAFNet [25]
MPRNet [248]
Face Restoration
FBCNN [78] (QF=5)
SwinlR [119]
X-Restormer [28]
Restormer [246]
X. Restormer [28]
Restormer [246]
NAFNet [25]
GFPGAN [200]
CodeFormer [271]
DifFace [244]
FBCNN [78] (QFw9))
FBCNN [78] (BQF)
SwinlR [119] (QF=40)
As previously mentioned in Appendix A. users can customize 4Kggent by adjusting the Restore Preference setting, which prioritizes either perceptual quality or fidelity. We achieve this by catego- rizing the methods in the restoration toolbox into perception-orienter -rriente and fidelity-orienter groups. For example, the Super-Resolution tools in the toolbox are split into:
1. -bdeeity-based: HAT-L [29]. X-Restormer [28], SwinFIR [253], HMA [34], DRCT [68]
2. Perception-based: DifIBIR [123], HAT-GAN [29], OSEDiff [216], PISA-SR [179], SwinIR (Real-ISR) [119]
Accordingly, when Restore Preference is set to Perception, 4KAgent restricts its execution to perception- methods, thereby aligning the restoration process with the user's preference.
We further develop a Fast4K mode for 4KAgent. Specifically, when the size of the input image at the current step of the restoration plan exceeds a predefined threshold st. 4KAgent automatically filters out methods with long inference times from the toolbox, such as DifBBIR (a 50-step diffusion- based method) in the super-resolution toolbox. Users can adjust 8, to control the running time 4K Agent. To comprehensively evaluate the performance of 4KAgent, Fast4K is disabled in all experiments.
A.3 Inference Details
A.3.1 Rollback
In this section, present the detailed workflow of the rollback mechanism in 4KAgent. If the quality score of 1 after step l in the initial restoration plan Pj is lower than a threshold y, ie., (T& S n. this step is regarded invokes the perception agent to adjust the subsequent plan based on the degradation list Dr, the remaining restoration tasks Aß of the restoration agenda A,, restoration experience E, and failure message Sy: P (P(DI.AA AR E, S1). After that, 4KAgent assigns an alternative restoration task for the current step. If all subsequent restoration tasks assigned to this step lead to rollback, 4KAgent adopts a fallback strategy and reverts to the original plan to execute subsequent tasks.
failure step and AKggtt generates failure message S,. Then 4K Agggtt
A.3.2 Implementation Details
Computational Resources, As a multi-agent system, 4KAgent supports multi-GPU deployment. Specifically, different agents (Perception Agent, Restoration Agent) are assigned separate GPUs to conserve memory. Most of our experiments are conducted using two NVIDIA RTX 4090 GPUs.
Hyperparameters in 4KAgent mainly reside in the Restoration Agent, including the weights used to compute the quality scores Q. and Q! in execution, as well as the quality threshold
Hyper-paramede
n for the rollback mechanism.. Specifically, we WNIQE 1.0, WMVSIQ 0.01, WMANIQA
1.0, WCLIPIQA 1.0 for Q., Wip 0.001, UIQA 1.0 for Q and 1) 0.5 for rollback.
A.3.3 Prompts
In 4KAgent, we enable the VLM LLLM to perceive image degradations and formulate a restoration plan via customized system prompts. In this section, present the details of these prompts.
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When the Perception Agent in the profile module selects DepictQA, we use the same prompt as in AgenticIR [274] for DepiciQA to assess the image degradations, and GPT-4 to generate the restoration plan. When Ilama-3.2- Vision is selected in the Perception Agent, we design tailored prompts for degradation reasoning and restoration planning, as shown below, where {-) represents slots to fill according to the context, and the content inside comes from external input. For the restoration experience in 4Kggent we employ the restoration experience from AgenticIR.
Prompt for Llama- Vision in Degradation Reasoning
You are an expert tasked with image quality assessment (IQA) and well-versed in popular IQA metrics, including CLIPIQA+, IOPIQ_NR, MUSIQ, and NIQE. Note that for NIQE, lower score indicates better image quality, whereas for the other metrics, higher scores generally reflect better quality. Here's an image to restcre, along with its corresponding quality scores evaluated using the aforementioned IQA metrics. First, please descrise the centent and style of the input image, the description must not contain its image quality. Second, please assess the image based en both the metric scores and your prior visual knowledge, with respect to the following two degradations: noise, motion blur, defecus blur, haze, rain, Jpeg compression artifact. Images may suffer from one more of these degradations. +Doo not output any explanations or comments.** **Strictly return only a JSON object** containing degradation types and image content/styli description. The keys in the JSON object should be: 'degradations' and 'image_description" Information about the input image: IQA metrics; (iqa_result). corresponds to Qr.)
Prompt for Llama- in Planning (Rollback)
You are expert in image restoration. Given imago of low quality, your task is guide the
utilize varicus tools to enhance its quality. The input image requires a list of restoration tasks. Your goal is to make plan (the order of the tasks) based on the task list. The final output shculd be formatted
JSON object containing the restoration plan (the correct oraer the tasks). The key the JSON object should be: 'plan'. Information about the input image:
Its description is: [image_description] (Ci),
suffer from degradations (degradations) (D1)
The list of restoration tasks: (tasks) (Ar / A/),
For your information, based on past trials, we have the fellowing experience in making a restoration plan: (experience) (E). Based on this experience, please give the correct order of the tasks in the restoration plan. The restoration plan must be permutation of (tasks)
the order you determine. (Besides, in attempts just now, we found the result is unsatisfactory, if (failed (failed_triess) (Sr) is conducted first. Remember not to arrange (failed tries) in the first place.) **Do not output any explanations or comments. vsttrictly return only a JSON object*x containing plan. The keys the JSON object should be: 'plan',
B Experiment Overview
We evaluate 4KAgent on variety of complex image restoration and super-resolution (SR) tasks to demonstrate its profile-driven flexibility under different restoration requirements, validate its generalization across multiple image domains, and quantify the contribution of each core component via ablation studies. Specifically, we evaluate 4KAgent across wide range of 11 image SR tasks on 26 benchmarks. The summary of datasets used experiments shown Tab. 6, which can be classified natural degraded images (SC,8D), Al- Al-generated images ($E), and scientific images ($F).
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Then, we perform an ablation study on Q-MoE policy and Face Restoration Pipeline in 4Kggent, together with a running time analysis (SG).
First, we evaluate 4K Agent on natural image restoration and super-resolution tasks under standard settings, including classical image super-resolution (4x) ($C.1), real-world image super-resolution (4x) ($C.2), consider more challenging scenarios, such as large scale factor super-resolution (16x) ($D.1) and joint restoration with 4K upscaling ($D.2). Finally, we extend 4KAgent to diverse domains by evaluating on AIGC images ($E.1) and scientific imagery (SF), including remote sensing ($F.1), 1),
image restoration ($C.3), and face restoration (4x) ($C.4). Then,
microscopy ($F.2), pathology (SF.3), and medical images ($F.4). To comprehensively evaluate the performance of 4KAgent, we disable the Fast4K mode in all experiments.
C Experiment Part I: 4x Natural Image Super-Resolution
C.1 Classical Image Super-Resolution
Settings In this section, we provide de-
Table 6: Dataset summary in 4KAgent experiments.
tailed experimental results on Set5 [13), Set14 [249], B100 [140], Urban100 [70], and Mangal09 [142] datasets. For a more com- prehensive comparison, we include recent methods, including HMA [34]. DRCT [68], and SwinFIR [253], as they are contained in the toolbox. In addition to metrics used in the main paper (PSNR, SSIM [208], LPIPS [262]. FID [66], NIQE [260), MUSIQ [83]). we employ DISTS [43], CLIPIQA [194), and MANIQA-pipal [233] for evaluation.
Images
Set5 [1.3]
Set14 [249]
14
Classical SR ($C.1)
B100 [140]
100
[70]
Manga109 [142]]
109
Real-
Real-World World SR ($C.2)
RealSR [17]
100
DreatSR [214]
MiO-Group [274]
Specifically, PSNR and SSIM are computed on the Y channel in the YCbCr color space and used to measure the fidelity of images. LPIPS and DISTS are computed in the RGB space and evaluate perceptual quality with reference images. FID is used to evaluate the distance of distributions between the ground truth and the restored images. NIQE, CLIPP- IQA, MUSIQ, and MANIQA- -ippal are used to evaluate the perceptual quality of images without reference images.
Multiple-Degradution IR ($C.3)
MiO-Group (274)
400
MiO-Group C [274]
400
Face Restoration (&C.4)
WobPbott- Test [200]
407
Large Scale Factor SR ($D.I)
RealSRRet [256]
20
Joint Imuge Restoration
DIV4K-50 (Ours)
Upscaling (8D.2)
Al-Generated
GcnAIBench-4K [103]
100
Content 4K SR ($E.1)
DiffusionDB-44 (2100
100
AID [223]
51
Sensing (8F.1)
DIOR [111]
DOTA [222
183
Quantitative Comparison It should be noted that, once the user sets the Restore Option to super-resolution in the profile, the 4KAgent system can be seen as quality- driven Mixture-of-Expert system for image super-resolution. In this mode, the system sequentially invokes every super-resolution tool in its toolbox based on the Restore Op- tion setting in the profile, then selects the best result based on the quality score Qe. Accord- ingly, we group 4KAgent with ExpSR-s4-F and ExpSR-s4-P profile into Fidelity- Fideed method and Perception-based method.
WorldStral [37]
85
Flnorescence Microscopy Imuge ($F.2)
SR-CACO-2
300
Pathology Image SR (SF:3)
beSR [77]
200
Chest X-may 2017 [85]
624
Chest X-my 14 [199]
880
Medical Image (8R4)
US-Case [175]
MMUSIK [154]
100
DRIVB [176]
20
Experimental results are shown in Tabs. 7 and 8. For the commonly used fidelity metrics PSNR and SSIM in the classical image SR task, 4KAgent with ExpSR-s4-F profile shows competitive performance compared to state-of-the-art fidelity-based methods, ranking among the top three on Set5, B100, Urban100 and Manga 109 datasets. For perception- -aaeee methods, we focus more on perceptual metrics such as NIQE, CLIPIQA, MUSIQ, and MANIQA. By simply switching the profile from ExpSR-s4-F to ExpSR-s4-P, 4KAgent achieves strong performance among state-of-
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Table 7: Quantitative comparison classical image super-resolution benchmarks (Set5, Set14, B100). The top three results for each metric are marked in bold, underline, and italic. For Agentic systems, only bold the best performance.
Dataset
Method
PSNR1
SSIM
LPIPS
DISTS)
FID
NIQEJ
CLIPIQAT
MUSIQU
MANIQA1
Fidelity-uueed method
SwinlK [119]
32,92
0.1567
0.6179
59.98
X-Restormer [28)
0.6368
[RCT[6S]
0.6406
HAT-L [29]
33.29
0.9082
0.1542
$6.95
7.11
0.6389
60.44
0.6212
HMA
33,39
0,9089
0.1587
0.1535
54.61
7.11
0.6338
60.39
0.6241
4gent (ExpSR-s4-FF)
33.34
0,9081
0,158V
0.1549
56.62
6,90
0.6294
60.02
0,6177
Perception-bosee methou
Ses5
(Real-ISR) (119
DiADBRR [123]
OSEDiIT [216]
0.7973
PISA-SR [179)
27.56
0.8189
0.1516
62,94
5.87
0L8996
69.87
0.6904
4KAgent (ExpSR-s4-P)
26.88
0.7899
0.159]
0.1657
70.63
5.79
0.8245
69.93
0.6808
Agenric System
AgentielR [274]
23.68
0.6711
0.2737
0.2190
124.96
6.59
0.7750
71.88
0.7079
4KAgent (GenSR-s4.P)
70.39
Fidelity- Saatey---eedddde method
SwinIR [119]
29.09
0,7950
0.26771
0.1574
70.49
6.19
0.5252
63.
0.5891
X. Restormer (28)
29.16
0.7963
0.2659
0.1557
69.36
6.22
0.5332
62.91
0.5925
DRCT [6S]
29.57
0.8009
0.2617
0.1524
67.84
6.09
0.5362
63.12
0.5932
HAT--I [29]
29.46
0.80144
0.2565
0.1516
66.61
6.11
0.5267
63.23
0.5986
HMA [34]
29.51
0.8019
0.2562
0.1510
69.41
6.25
0.5278
63.00)
0.6012
4KAgent (ExpSR-<4-F)
0.5315
Perception-basee method
Se114
SwinlR (Real-1SR) [1
25.91
0.7187
0..244
0,1508
96.19
4.45
0.6500
66.82
0.60154
DiNBIR [123]
24.73
0.6349
0,2338
0.1545
100.51
4.34
0,7353
72.97
0.6869
OSEDid [216]]
24.30
0.6663
0,2389
0.1524
101.03
4.61
0.7264
70.02
0.6674
PISA-SR [179]
24.76
0.6716
0.1993
0.1343
89.91
4.16
0.7643
71.81
0.7015
Agont (ExpSR-c4-P)
24.76
0.6471
0.1467
101.
72.54
0.6956
Agentic System
AgentieIR [274]
21.98
0.6064
0.2807
0.1812
129.29
4.58
0,7419
72.48
0.6804
4KAggent (GenSR-s4-PP
23.40
0.63440
0.2484
0.1749
125.29
4.29
0.7604
73.64
0.7061
Fidelitybaveed method
SwinIR [119]
27.92
0.7489
0.5373
X-Restormer [28)
27.99
0.7508
DRCT [68]
28.10
0.7535
6,06
(1,5999
58.78
0.5895
HAT-
28.08
0.7547
0.3440
0.1952
89.52
6.20
0.5477
58.71
0.5991
HMA [34]
28.12
0,7559
0.3442
0.1953
88.46
6.17
0.5534
39.11
0.6043
4Kegent (ExpSR-s4-F)
28.09
0.7540
0.345.
0.1950
88.89
6.02
0.5516
$9.12
0.5994
Perception--ouee method (Real-ISR)
B100
25.42
0.6711
0.6322
62.78
DiMBIR [123] OSEDiY [216]
0.1666
3.92
24.35
0.6495
0024488
0.16.4
73.23
4,08
0.7422
68.54
0.6725
PR [179]
25.00
0.6520
0.2111
0.1471
61.82
4.04
0.7384
68.47
0.6829
4K.g (ExpSR-s4-PP)
24.64
0.6294
0.2387
0. 1606
73.64
3.86
0.7546
69.42
0.6851
Agentic System
AgsonieR [274]
22.51
0.1907
4KAgent (GenSR-s4PP)
0.7354
the-art perception- based methods, ranking among the top two across most metrics on all classical SR benchmarks. For comparison across agentic systems, 4KAgent outperforms AgenticIR in most metrics on classical SR benchmarks, especially on the Set14 and B B100 datasets.
Qualitative Comparison For visual comparison, we select two leading fidelity- methods (X-Restormer, HAT-L), two perception-based methods (SwinIR (Real-ISR). DiffBIR), as well as one agentic system (AgenticIR) baselines. For Agent, we present result images under GenSR-s4-P profile for comprehensive comparison. Visual comparisons in Fig. 9 reveal that fidelity- based
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Table 8: Quantitative comparison classical image super-resolution benchmarks (Urban and Mangal09). The top three results for each metric are marked bold, underline, and italic. For Agentic systems, we only bold the best performance.
Dataset
Method
PSNRT
SSIM1
LPIPS1
DISISL
FID
NIQE
CLIPIQA:
MUSIQT
MANIQAT
Fidelity-aazed method
SwintR [119]
0.184D
X-Restermer [28]
DRCT [68]
HAT-L [29)
28.58
0.8495
0.1598
0,1411
5.55
0.5054
20.62
0.6906
HMA [54]
28.69
0.8511
0.1583
0,1405
2.93
5.61
0,508V
70.75
0.6893
4KAgtent (ExpSR-s4-F)
28.59
0.8479
0,159
0,1399
2.977
5.31
0.5235
70.70
0.6833
Perception-haved method
Urban100
SwintR (Real-ISR) [119]
0.1998
0.2011
OSEDiN [216]]
PISA-SR [179)
22.36
0.6704
0.1823
0.1297
28.51
4.43
0,6814
72.93
0,7020
4KAggen (ExpSR-s4-P))
22.56
0.6582
0.1955
0.1378
25.55
4.53
0.7092
73.65
0.6981
Agentic System
AgeuticlR [274]
22.03
0.6615
0.2147
0.1507
31.09
4.65
0.6790
73.10
0.6873
4KAgent (GenSR=s4.P)
Fidelity-based method
SwintR [119]
32,05
0.9260
0.0926
0.0761
1.88
5.32
0.6385
70.32
0.6117
X. Resettrrer [28]
32.40
0.9279
0.0909
0.0748
1.88
5.48
0.6325
20.05
0,6123
DRCT [68]
32.84
0.9307
0.0889
0.0685
1.49
5.08
0.6362
69.77
0.6087
HAT-L [29]
33.08
0.9334
0.0455
0.0844
1.4%
5.26
0.6160
69.76
0.6145
HMA [34]
33.20
0.9344
0.8835
0.0682
5.24
0.6208
69.92
4KAgent (ExpSR.s4-F)
Perceptionhhaved method
Manga109
SwintR (Real-1SK)
26.29
0,8553
0.1367
0.0948
24.59
4.30
0.6316
700.28
0,5868
DifEBIR [123]
23.57
0.7297
0.1923
0.1275
30.11
4.55
0.7804
74.51
0.6787
OSEDidf [216]
23.74
0.7980
0.1703
0.118)
41.54
4.78
0.6874
72.51
0.6538
PISA SR [179]
24.02
0.8119
0.1450
0.1161
34.11
4.35
0.7277
74.76
0.6779
Akkntt (ExpSR-s4-P)
24.76
0.7615
0.1776
0.1231
4.32
0.7678
Agentic System
ApcntielR [274]
23.70
0.7550
0.1862
0.1246
34.01
4.38
0.7450
73,98
0.6597
4KAgent (GenSR-s4-P)
23.12
0.7556
0.1834
0.1264
34.58
4.23
0.7652
75.02
0.6797
10
Resormerr
HATE
SwinIR (Real ISR)
DUNBUR
AgenneIR
4KAAgent (GeaSR -4-FF
HQQ
DimRAlI
Ageniclit
4K 4KrAge (GenSR-sl-P")
Figure 9: Visual comparisons the classical image SR task (please zoom in to details).
methods tend to produce overly smooth or blurred details (e.g., HAT-L.), even when trained under real- world image SR settings (e.g.. SwinIR (Real-ISR)), which is visually unplcasant. The diffusion-basee
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method DiffBIR generates rich but unrealistic details. AgentieIR performs well in detail generation but still lacks realism and exhibits noticeable color shifts. 4KAgent delivers both richer and more accurate details than these methods. For instance, it faithfully reproduces the fine stripes on tree bark in the top row and the intricate structure of antlers in the bottom row.
Discussions In the context of classical image super-resolution (SR), fidelity-based methods priori- tize reconstruction accuracy, measured by PSNR and SSIM, resulting in outputs that often appear overly smooth or blurred. In contrast, perception-basee methods optimize for high perceptual quality, reflected in metries like NIQE, CLIPIQA, MUSIQ, and MANIQA, though often the expense of fidelity. For example, diffusion- based approaches (e.g., DiffBIR) may hallucinate rich but unrealistic textures. AgentielR, while capable of generating sharper details, sometimes introduces color shifts or artifacts that undermine visual plausibility. 4KAgent offers configurable flexibility through its profile system, allowing to operate either as a fidelity-bane -hiriy---dddd--- system (ExpSR-s4-F) or as perception system (ExpSR-s4-P). Quantitatively, 4KAgent delivers competitive PSNR and SSIM scores under the fidelity-based profile, and achieves leading performance in perceptual metrics (NIQE, CLIPIQA, profile. Qualitatively, 4KAgent consistently produces
MUSIQ, MANIQA) under the perception- images with rich, realistic details. The flexibility of Ageget allows it to strike a superior balance: can be easily tuned for maximum visual fidelity or for maximum perceptual appeal without extra training or adaptation, which avoids the common draw backs of existing SR systems.
C.2 weal-World Image Super-Resolution
Settings In this section, we provide a detailed analysis of 4KAgtt in the real-world image super- resolution task by presenting detailed experiment results on both the RealSR and DRealSR datasets, as well as visual comparisons.
Quantitative Comparison Experiment results on real-world image super-resolution datasets shown in Tab. 9. For this task, we focus more on perceptual metrics, such as NIQE, CLIPIQA, MUSIQ, and MANIQA. Real-world image SR methods have achieved promising results on these metrics. AgenticIR, which contains the DitIBIR in its toolbox, outperforms DiffBIR in most perceptual metrics, indicating that agentic systems have better potential in solving the real-world SR problem. 4KAgent goes a step further and outperforms AgenticIR in most metrics, achieving better perceptual quality with better fidelity (e.g.. PSNR, SSIM), regardless of profile setting. In addition, 4KAgent sets a new state-of-the-as performance on perceptual metrics (e.g., NIQE, MUSIQ)
Table 9: Quantitative comparison on real-world image resoluttnnn benchmarks (RealSR and DrealSR). The top three results for each metric marked in bold, underline, and italic.
Dataset
Method
PSNR1
SSIM
LPIPS
DISTS!
FID.
NIQE!
CLIPIQA
MUSIQ
MANIQA1
ResSliff [245]
26.31
0.7411
0.3489
0,2498
142.81
7.27
0.5450
58.10
0.5305
StableSR [195]
24.69
0.7052
0.3091
0.2167
127.20
5.76
0.6195
DiIBBR [123]
24.88
0.6673
0.3567
0.2290
124.56
5.63
0.6112
64.66
PASD [234]
25.22
0.6809
0.3392
0.2259
123,08
5,18
0.6502
68.74
0.6461
SccSR [217]
25.33
0.7273
0.2985
0,2213
125 66
5.38
0.6594
69.37
0.6439
RealSR
SinSR [205]
26.30
0,7354
0,3212
0,2346
137.05
6.31
0.63/4
60.41
0.5389
OSED [216]
25.15
0.7341
0.2921
0.2128
123.50
5.65
0.6693
69.09
0.6339
PISA-SR [179]
25.50
0.7417
0.2672
0,2044
124.09
5.50
0.6702
70.15
0.6360
AgenticIR [274]
22.45
0.6447
0.3745
0,2503
140.38
5.81
0.6506
65.87
0.6210
4KAgent (ExpSR-s4-P)
24.60
0.6839
0.3253
0,2292
127.64
5.09
0.7078
70.97
0.6602
4K.geet (GonSR-s4-P9)
22.55
0.6557
0.3509
0.2468
134.63
4.78
0.6666
71.77
0.6564
ResShin [245]
28.45
0.7632
0.4073
0,2700
175.92
8.28
0.5259
49.86
0.4573
StableSR [195]
28.04
0.7460
0,3354
0.2287
147.03
6.51
0.6171
58.50
0.5602
DamBRR [123]
26.84
0.6660
0.4116
0.2706
167.38
6.02
0.6292
60.68
0.5902
PASID (234)
27.48
0.7051
0.3854
0,2535
157.55 36
5.57
0.6714
64.55
0.6130
SeeSR [217]
28.26
0.7698
0.3197
0.2306
149.86
6.52
0.6672
64.84
0.6026
DreatSR
SinSR [205]
28.41
0.7495
0.3741
0,2188
177.05
7.02
0.6367
55.34
0.4898
OSFDiif [216]
27.92
0.7835
0.2968
0.2166
135.29
6.49
0.6963
64.69
0.5899
PiSA-SR [179]
28.31
0.7804
0.2169
130.61
6:20
0.6970
66.17
0.6756
AgenticIR [274]
23.06
0.6145
0.4775
0,2973
182.02
6.11
0.6842
63.59
0.5927
4KAgent (ExpSR-s4-P)
26.00
0.6535
0.1257
0.2717
170.19
5.51
0.7167
67.72
0,6397
4KAgent (Gen8R.e4-P))
23.11
0.6126
0.4579
0,2896
178.36
4.65
0.7092
69.30
0.6219
33

---

Qualitative Comparison For visual comparison, we select four representative real-world image super-resolution methods (StableSR, DifBBIR, SinSR, OSEDiff) as well as one agentic system (AgentielR) as baselines. The visual results are presented in Fig. 10. While these methods are able to recover rich details from the LQ image, their results often lack realism and visual fidelity. For example, in the top row, OSEDiff reconstructs clothing that appears more like jackets, whereas the HQ reference image shows down jackets. 4KAgent produces sharper and more realistic details, such as the texture of the down jacket in the top row and the clarity of the number '27' in the bottom row.
StallSR
OSEDi!!
AgentieIR
4KAgeat (GenSR-a4-P)
HQ
LQ
StableSR
DiMBIR
SinSk
22
27
27
OSEDiE
AgenticIR
4KAgent (GenSR-at-P)
HQ
Figure 10: Visual comparisons on the real-world image SR task (please zoom in to see details).
Discussions The real-world image super-resolution task is substantially more challenging than the classical image super-resolution task, involves complex and unknown degradations beyond synthetic downsampling processes, which can also seen from the comparison of the LQ image and HQ image in the dataset. Under this challenging setting, agentic systems prove their advantage by analyzing the distortion and restoring the image properly. 4KAgent further proves its superiority by consistently outperforming AgenticIR in most quantitative metries. In particular, 4K Agggtt sets new state-of-the-art performance for no-reference perceptual metrics, demonstrating that its design effectively elevates perceived realism. Qualitatively, these gains translate into visibly sharper and more believable details, By dynamically leveraging multiple SR experts and selecting the optimal result, 4KAgent shows its superiority in the challenging real-world image super-resolutio task.
C.3 Multiple-Degradation Image Restoration
Settings In this section, we present detailed experimental results on Group A, B, and C test sets from the Mi0100 dataset [91] with more visual comparisons.
Quantitative Comparison Experimental results shown in Tab. 10. In the multiple-degradatinn image restoration (IR) task, agentic systems once again prove their superiority, outperforming all- in-one methods in all metries. Among the agentic systems, 4K 4KAgent performs the best, achieving a new -fate-of-the-ar performance on PSNR, MANIQA, CLIPIQA, and MUSIQ. Specifically, for no-reference perceptual metrics (MANIQA, CLIPIQA, MUSIQ), 4KAgent outperforms all compared methods by noticeable margin (e.g.. 4.2 lead in MUSIQ on Group C). For SSIM and LPIPS metrics, Group A and Group C subsets.
4KAgent remains competitive, ranking within the top
Qualitative Comparison visual comparison, we select two leading all-in-one methods (DA- CLIP, AutoDIR) as well as agentic system (AgenticIR) as basclines. Visual comparisons shown
34

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Table 10: Quantitative comparison of multiple-degradation image restoration tasks three subsets (Group A, B, and C) from the MiO100 dataset. The top three results for each metric are marked in bold, underline, and italic.
Degradations
Method
PSNR1
SSIM
LPIPS
MANIQAT
CLIPIQAT
MUSIQ1
AirNet
19.13
0.6019
0.4283
0.2581
0.3930
42.46
PromptiR [162]
0.6088
0,4013
M6OIR [90]
0.6558
[136]]
Group A
Instmueti [36]
18.03
0.5751
0,4429
0.2660
0.3528
45.77
AutoDRR [81]
19.64
0.6286
0,3967
0.2500
0,3767
47.01
AgenticIR [274]
21.04
0.6818
0.3148
0.3071
0.4474
56.88
MAIR [80]
21.02
0.6715
0.2963
0.3330
0.4751
59.19
4K Agentt (GenMIR-P)
21.48
0.6720
0.3019
0.3748
0.5544
63.19
AirNet
0.6567
0.2882
PromptIR [162]
20.47
0.6704
0.3370
0,2893
0,4289
48.10
MIOIR [90]
20.56
0.6905
0.5243
0,2638
0,4350
51.87
DA-CLIB LP [136]
18.56
0.5944
0.4405
0.2435
0,4155
43.70
Group B
InstructR [36]
18.34
0.6235
0.4072
0,3022
0.3790
50.94
AutoDIR [81]
19.90
0.6644
0.3542
0.2534
0.3986
49.64
AgenticIR [274]
20.55
0.7009
0.3072
0.3204
0.4648
57.57
MAIR [80]
20.92
0.7004
0.2788
0.3544
0.5084
60.98
Agenttt (GemMIR-P)
20.95
0.6727
0.3017
0.3734
0.5505
62.69
AidNet [107]
17.95
0.5145
0.5782
0,1854
0.3113
30.12
PromprIR [162]
18.51
0.5166
0.5756
0.1906
0,3104
29.71
MEOIR [90]
15.63
0.4896
0.5376
0.1717
0.2891
37.95
DA-CLIP [136]
18.53
0,5320
0.5335
0.1916
0,3476
33.87
Group C
InstmuctRR [36]
17.09
0.5135
0.5582
0.1732
0.2537
33.69
AutoDIR [81]
18.61
0.5443
0.5019
0.2045
0,2939
37.86
AgenticlR [274]
18.82
0.5474
0.2698
0.3944
[80]
0,5544
4Kggen (GenMIR-P)
19.77
0.5629
0.4271
0.3545
0,5233
$3.56
LQ
AgenticIR
4KAgent (GenMIR-P)
HQ
LQ
DA-CLIP
AutoDIR
AgenticIR
4KAgent (GenMIR-PP)
HQ
Figure 11: Additional visual comparisons on the Mi0100 dataset (please zoom in to see details).
35

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in Fig. 11. All-in-one methods exhibit limited performance in this setting, especially when restoring complex distortions, such raindrops. AgenticIR achieves promising results, demonstrating the potential of agentic systems in dealing with complex distortion tasks. 4KAgent goes a step further by generating images with finer-grained details and better consistency with the high-quality (HQ) reference image. For instance, the natural stripes on the tree trunks and the fine leaf textures in the lop row, as well as the intricate waterfall ripples and mountain contours in the bottom row.
Discussions Based the experimental results, agentic systems have shown their superiority in the multiple-degradation image restoration tasks, where each low-quality (LQ) image is affected by 2 ~ 3 types of distortions. Under this challenging setting, 4KAgent exhibits clear advantage in handling complex, multi-degraded inputs, outperforming both conventional all-in-one methods and previous agentic systems. Quantitatively, AKggent achieves state-of-the of-the-arttttttt results across multiple metrics, including PSNR, MANIQA, CLIPIQA, and MUSIQ, highlighting its strong capability in enhancing both fidelity and perceptual quality. Qualitatively, this metric superiority translates into more faithful restoration of fine-grained patterns and textures, even under severe and heterogeneous distortions.
C.4 Face Restoration
Settings In this section, evaluate 4KAgent the real-world face restoration benchmark, the WebPhoto-test [200] dataset, which contains 407 low-quality face images collected from the Internet. As the face restoration pipeline a module subsequent the super-resolution task in 4KAgent, we first downsample the images by a factor of 4 to generate low-quality (LQ) images. In this experiment, we configure 4Kggt with the GenSRFR-s4-P profile.
We compare 4KAgent with state-of of- ttthhheartt art face restoration methods, including CodeFormer [27 I], GFPGAN [200], and DifFace [244], well as agentic system AgenticIR [274]. For face restoration methods, we set the scaling factor to 4. As there are no high-quality (HQ) references, we evaluate performance with four no-reference perceptual metrics (NIQE, CLIPIQA, MUSIQ, and MANIQA- pipal) and two advanced face-specific IQA metrics (CLIB-FIQA [156] and DSL-FIQA [27]).
Quantitative Comparison Experimental results are shown in Tab. 11. AgenticIR performs inferior to prior face restoration methods in terms of no-reference perceptual metrics and face IQA metrics. 4KAgent outperforms AgenticIR on every metric by a clear margin (e.g.. 19.67 lead on MUSIQ). Moreover, 4KAgent achieves the best performance on no-reference perceptual metrics and delivers competitive results on face IQA metries, ranking second on both CLIB-FIQA and DSL-FIQA.
Table 11: Quantitative comparison on face restoration benchmark (WebPhoto- Test). The top three results for each metric are marked in bold, underline, and italic.
Dataset
Method
NIQEL
CLIPIQA
MUSIQU
MANIQA!
CLIB-FIQA
DSL-FIQA1
GFPGAN [200]
74.21
0.6379
0.6590
Codetiomer [271]
4.58
0.6844
78.87
0.6415
0.6840
0.7435
WebPheto-Test
Difface [244]
4.20
0.5831
65.31
0.589]
0.6511
0.6189
AgenticIR [274]
6.85
0.5731
56.25
0,5465
0.5978
0.5289
4KAgont (GenSRFR.s4.PP
4.15
0.7077
75.92
0.6576
0.6671
0.7683
Qualitative Comparison Visual comparisons are shown in Fig. 12. Compared with other methods, 4KAgent demonstrates a clear advantage in restoring realistic facial details, such as fine hair strands and natural skin textures. Moreover, it achieves superior restoration performance in non-facial regions, such as the wall and leaves in the first row and the logo on the hat in the second row. By consistently delivering high-quality restoration in both facial and non-facial areas, 4KAgent produces more visually pleasing and perceptually balanced results overall.
Discussions In the face restoration scenario, 4KAgent effectively addresses both facial and contex- tual degradations. Quantitatively, 4KAgent achieves the best performance on general no-reference perceptual metrics and delivers competitive scores on face IQA metries, demonstrating the superiority of its system design and face Q-MoE policy. Qualitatively, this translates into more natural and richly detailed facial features, such as individual hair strands and realistic skin texture, while also enhancing background elements, producing more visually pleasing outputs. Among agentic systems, AgentieIR
36

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LO
GFIGAN
CodeFornet
DicFwee
AgentiiR
4KAggett (GonSRFR-s4-P)
Figure 12: Visual comparisons on the face restoration task (please zoom in to see details).
applies uniform processing pipeline without a dedicated face restoration module, which limits its performance in this scenario. Benefiting from the face restoration pipeline and profile design, 4KAgent can be tailored as a face restoration expert, achieving superior results. We further present how these two designs enhance the face restoration ability of 4KAgent in the ablation study.
D Experiment Part II: 16x Natural Image Super-Resolution
Experiment Part (SC) demonstrates the flexibility and superiority of 4KAgent on general image restoration and super-resolution tasks. In this section, we evaluate Agege on more challenging restoration tasks. First, we assess its performance on the 16x real-world image super-resolution task. Next, we evaluate AKegtt on our proposed DIV4K-50 dataset.
D.1 Large Scale Factor (16x) Image Super-Resolution
Settings In the main paper, we present visual comparisons between 4KAgent and state-of-the-arr image super-resolution methods (e.g., HAT-L, DiftBIR) and agentic systems (e.g., AgenticIR) on the RealSRSet dataset under a large-scale factor (16x) upscaling setting. Here, extend experiment with detailed quantitative evaluations with more methods and additional qualitative examples. As there corresponding high-quality (HQ) reference images in the dataset, evaluate the result images on four no-reference perceptual metrics: NIQE, CLIPIQA, MUSIQ, and MANIQA-
Quantitative Comparison Experimental results shown in Tab. 12. As the largest scale factor of the pre-trained model HAT-L is 4, we apply the 4x - 4x setting for HAT-L for 16x upscaling. Fidelity-based method struggles deliver satisfactory performance on perceptual metrics under this setting. Recent perception-base based real-world image super-resolution methods perform well on these metrics, even with the 16x setting. For example, DitTBIR with 16x setting achieves the best NIQE and MANIQA scores. Among agentic systems, 4KAgent outperforms AgenticIR on every metric by clear margin (e.g., 6.13 lead on MUSIQ), In addition, 4KAgent achieves the best performance on MUSIQ and the second-bec best performance on NIQE. For CLIPIQA and MANIQA metrics, 4KAgent also delivers competitive performance, ranking among the top three across all methods.
Table 12:
Quantitative comparison on RealSRSet dataset under 16x upscaling. The top three results for each metric are marked in bold, underline, and italic.
Dataset
Method
NIQEL
CLIPIQA!
MUSIQU
MANIQAT
HAT-L [29] 4x)
DiTRIR (4x
DiABIR [123] (16x)
2.80
0.7583
47.54
0.6099
OSEDiT [216] (4x
5.40
0.7665
48.42
0.5362
RealSRSet
OSEDif [216] (16x)
4.66
0.6483
35.33
014581
PISA-SR [179] 64 4x)
5.70
0.7883
48.20
0.5464
P.SA-RR [179] (16x)
4,88
0.6384
35.90
0,4128
AgentilR [274]
4.86
0.6775
44.71
0.5236
4KAgent (Geo4K-P))
3.53
0.7794
50.84
0.5913
37

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LQ
HAT-L (4x - 4x)
DIMBIR (16x)
AgentieIR
4KAgent (Gen4K-P)
Figure 13: Additional visual comparisons on the RealSRSet dataset (please zoom in to see details).
Qualitative Comparison For visual comparison, we select three representative methods to bench- mark against 4KAgent: (1) HAT-L (4x-> 4x): As a representative fidelity- -bsted method, we investigate its performance under large-scale scale upscaling setting.
(2) DiffBIR (16x): As shown in Tab. 12, DiffBIR with 16x setting achieves the best performance on NIQE and MANIQA. There- fore, we include it to assess its visual quality. (3) AgenticIR: Selected for agentic system comparison. Visual comparisons are shown Fig. 13.
HAT-L (4x + 4x) shows limited enbancement over the low-quality input, leading to notably blurred textures. DifIBIR (16x) produces visually rich but often unrealistic hallucinations, and in some cases even alters the semantic content of the scene (e.g. the first row), which is visually unappealing. AgenticIR generates visually plausible results but lacks sufficient sharpness and fine- fne-grained details. 4KAgent generates fine-grad tnee-graine and realistic details: the rock and grass textures in the first row, and the hair strands, eyebrow patterns, and naturally expressive eyes the second and third rows are all much more faithfully restored with finer- mner-grained details.
Discussions In the challenging 16x upscaling scenario, 4KAgent delivers competitive quantitative results alongside fine tne-graine and realistic qualitative results, compared to other methods. In addition, traditional aedelity-oriented methods such HAT-L struggle to recover fine details and instead produce overly smoothed and blurred results. This highlights the limitation fidelity-driver pipelines under extreme magnification levels. Therefore, for such high-scale upscaling tasks, it is essential to configure 4KAgen with a perception-oriented profile (e-g. setting Restore Option to Perception in the profile module) to better prioritize realistic texture synthesis. As image resolution approaches 4K and beyond, existing no-reference perceptual metrics may become misaligned with human judgment of visual quality. This discrepancy underscores the need for developing new no-reference perceptual metrics specifically designed for ultra-high-resolution images.
D.2 Joint Restoration & 4K Upscaling
Settings In this section, we bring 4KAgent to the most challenging setting: Joint multiple image restoration and 4K upscaling. As there are previous methods and datasets targeted at this setting, we propose a new evaluation dataset, D5V, constructed from the Aesthetic-4K dataset [254] to rigorously test end-to-end restoration and iitr--high-saal SR. In this experiment, evaluate 4KAgent on DIV4K-50 and we configure 4K Agent with the Gen4K-P profile. Comparing methods and experimental settings are the same in Appendix D.I.
Quantitative Comparison Quantitative comparisons are shown in Tab. 13. Similar to the experi- ment results in Appendix D. real-world image super-resolution methods perform competitively on perceptual metrics under this challenging setting. For example, DifBBIR achieves the best score on NIQE and CLIPIQA metrics. For agentic systems, 4KAgent outperforms AgenticIR on every metric. Additionally, 4K Agent achieves the best performance on MUSIQ and MANIQA metries, and the second-best best performance on NIQE and CLIPIQA metries.
38

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Table 13: Quantitative comparison DIV4K-50 dataset. The top three results for each metric marked in bold, underline, and italic.
NIQEL
CLIPIQAT
MUSIQU
MANIQAT
HAFL 64x 4X)
11.86
0,1699
22.82
0.3270
DiABER [123] (4x - 4x
3.36
0,7588
37.17
0.5916
DIMBIR [123] (16x)
2.65
0,7078
38.39
0.5858
OSEDif [216] (4x
4.88
0.7201
39.88
0.5482
DIV4K-50
OSEDiE [216] (16x)
8.37
0.5680
25.07
0.4210
PSS--K [179] - 4X)
0.7141
38.22
0.5364
[179]
AgentiIR [274]
0.4814
4K (Gen4K-P)
3.15
0.7585
44.16
0.5928
HAT-L 4X)
DIADIR (4X 4X)
OSEDiA 4X)
PISA-SR (1X 4X)
AgenteIR
4KAgent (Gen4K-P)
HQ
LQ
HAT-L (x)
DinBIR (4x (x)
OSEDiT (4X 1X
PISA-SR (4x - 4x)
AgenticIR
4Kggent (Gen4K-P)
HQ
Figure 14: Additional visual comparisons on the DIV4K-50 dataset (please zoom to see details).
Qualitative Comparison shown in Appendix D.I, directly upscaling images with the 16x setting often produces visually rich but unrealistic artifacts. To ensure fair and meaningful qualitative comparison in this experiment, we select previous methods with the 4x + 4x setting, along with AgenticIR, as baselines. Qualitative comparisons are shown in Fig. 14. Real-world image super- resolution methods generally recover more details than fidelitybbssd method. However, their outputs still exhibit noticeable distortions. For example, the generated patches from OSEDiff and PiSA-SR the middle row retain visible JPEG compression artifacts, which degrade the overall visual quality, While DIffBIR achieves the most favorable visual results among these methods, its outputs still suffer from either blurring or unrealistic artifacts. AgentieIR performs competitively but tends to produce insufficiently sharp details. 4KAgent consistently reconstructs finer and more natural details. Figure 14 presents additional visual comparisons: even this challenging setting, 4KAgent faithfully reconstructs finer, more natural details, such the bears fur in the top row and the intricate mountain textures in the bottom row, highlighting the superiority of our method.
Discussions The DIV4K-50 benchmark presents an extremely challenging setting, where fully recovering low-quality x56x256 inputs match the 4096x ground-truth images is virtually
39

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unattainable. Therefore, our focus shifts towards generating richly detailed and visually authentic textures rather than exact pixel-wise reconstruction. Existing real-world image super-resolution methods struggle to fully correct the compounded degradations present in challenging scenarios. While these methods achieve competitive scores on perceptual metrics, their outputs tend to suffer from unrealistic hallucinated textures. Prior agentic systems, while effective at handling multiple degradations, show limitations in maintaining sufficient sharpness when upscaling to 4K resolutions. 4KAgent demonstrates its capability to simultaneously address multiple degradations and extreme scaling factors, effectively reconstructing natural, fine-grained details with high visual fidelity.
E Experiment Part III: AI-Generated Content (AIGC) 4K Super-Resolution
Text-lo-visual models have ushered in new era of high- high-quality image synthesis in Al- Al-generated content. While existing models exhibit impressive capabilities in interpreting and following complex user instructions, their limitation to relatively low-resolution outputs (e.g. 1024 x 1024) poses significant challenges for applications requiring ultra-high visual fidelity, such as digital content creation and cinematic production. Scaling diffusion models for nigh-resolution generation entails computational overhead and access to large-scale high-resolution training data. As practical alternative, pre-trained diffusion models can be repurposed for ultra- ultra-high-resee high---i--rrrottionn image generation.
E.1
AI-Generated Content 4K Super-Resolutiog Experiment
In this section, present comprehensive experimental results comparing the effectiveness of end-to- end generation of ultra-high-resolution images with upscaling 1K images to 4K using our Ageget. The comparsions are conducted
curated datasets: GenA.Bench-4K and DiffusionDB-44.
Settings We curated set of 200 prompts sampled from two widely-used AIGC benchmarks [210, 103]. For each prompt, IK-resolution images were generated using several representative text-to- image models, including Flux. 1-dev [95], Stable Diffusion 3 (SD3) [49], PixArt-E [24], SANA [229], and GPT-4o [72]. parallel, we employed native 4K-capable models such as SANA [229] and Diffusion-4K [254] to directly synthesize 4K-resolution outputs. Due to more stringent safety protocols, GPT-4o yielded only 39 valid IK-resolution images from DiffusionDB prompts.
We use the ExpSR-s4-P profile in 4KAgent here. To assess perceptual quality, we employ no- reference perceptual metrics. However, we observe that these metrics, particularly MUSIQ, tailored for evaluating ultra-bigh-resolution images, likely due to their inability to capture fine-grained details through a multi-scale architecture. To mitigate this limitation, we introduce MUSIQ-P, patch-applics variant that computes MUSIQ scores over non- overlapprinn 512 x 512 patches and averages them, thereby improving sensitivity to localized artifacts in ultra-high-ressluttin content.
Table 14: Comprehensive quantitative comparison of AIGC 4x
The top three performances of each metric are marked in bold, underline, italic respectively. MUSIQ-P" indicates a patch-applied variant of the MUSIQ metric for evaluating ultra-bigh-resolltion (4K) images.
Dataset
GenAIBench-4K [103)
DiffusionDB-4 (210)
Model
NIQEJ
CLIPIQAT
MUSIQ-P"T
MANTQAT
NIQEL
CLIPIQAT MUSIQ-P'T MANIQAT
SANA 4K [229]
0.6172
47.93
48.66
0.3425
Diffusion-IK [254]
6,38
0.5049
35.07
6.55
0.5056
35.87
0.3404
SANA-IK [229]
4.18
0,7147
66.30
0,4814
3.80
0.6910
67.99
0,5104
4KAgent
3.03
0.7050
57.97
0.4735
3.04
0.7082
60.48
0.4715
GPT4e [72]
5.69
0.6607
64.43
0.4997
5.13
0.6275
62,53
0.4398
4KAgent
3.56
0,7016
58.28
0.4976
3.40
0.6867
56.67
0.4711
FLUX.I-4K 1951
0.6768
61.02
0.7509
0.5835
4KAgent
2.98
0.7078
58.19
0,5034
3.04
0.7440
60.88
0.5056
PixAn-E 124]
4.12
0.6960
63.74
0.4415
3.66
0.6892
66.54
0.4386
4KAgent
2.76
0,7077
56.71
0.4699
2.88
0.7092
58.85
0.4659
SD3-Medium [49]
5.03
0.6922
64.68
0.4767
4.38
0.6667
65.99
0.4413
4KAgent
0.7169
60.22
2.99
0.7066
59.35
40

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Quantitative Comparison. Tab. 14 presents the quantitative results across three strategies: (1) native 4K generation,
(2) IK-resolution generation, and (3) IK-resolution images upscaled by 4Kggett. On GenAlBBnch-4K, the SANA-IK + 4KAgent pipeline achieves NIQE score of 3.03 and a CLIPIQA of 0.7050, significantly outperforming SANA-4K (NIQE 4.02, CLIPIQA 0.6172). Similarly, PixArt-E + Agegett obtains the best NIQE score (2.76) among all methods, while SD3-Medium + 4KAgent achieves the best CLIPIQA (0.7169) and MANIQA (0.5155) scores. On DiffusionDB-44, 44,K, several models such SANA-IK, GPT-4o, PixArt-E, and SD3-Medium, when upscaled with 4gegtt, achieve significantly lower NIQE and higher CLIPIQA scores. Although MUSIQ-P scores for the upscaled images show slight decrease relative to their 1K counterparts, the difference remains marginal, suggesting limited perceptual degradation during upscaling.
To further assess semantic and aesthetic fidelity, we report PickScore [88] in Tab. 15, which quantita- tively captures diversity and auman-aligned visual quality. On Gen AlBench-K, models enhanced with Agggt outperform their native 4K counterparts. On DiffusionDB-44 the performance gap is smaller, which may be attributed the dataset's richer and more descriptive prompt content.
Table 15: Comparison PickScore-Band Upscaled IK Models.
[88] Quantitative Evaluation Between Native 4K and 4Kggent-
Dataset
Avg. Prompt Length
SANA-4K
SANA-IK + 4KAgent
Diffusion-4K
Flux.1-dey + 4KAgent
GenAIBench--4 [103]
0.2389
0.7611
DiffusionDR 4K [210]
25.29
0.4893
0.5107
0.2406
0.7594
Diffusion-4K
GPT4o AKAgent
Diffasion-4k 4K
Flux Agent
An action photograph of cat casting for out if it's mouth.
A magician pulling rabbit out of hut.
SANA-
PixA igma 4K4gent
SANA 4K
Diffusion-4K
Stall Dr brins th Agdir
Diffusion-dh
Elas JK. AgAgu
Hyperszalitic pectralt of philippiee durader exnic am inaance,e rais, by steblam, sean, quint, ydlow
Tidal wave phlegm.
SANA-IK
PixArt-Signn Siggaa
4g.g
PixArt Sigma JAAgget
Figure 15: Visual comparison between native 4K image generation and IK image generation methods with 4KAgent, using identical prompts. 4K images from Diffusion-4K and SANA-4K are displayed on the left. while the corresponding outputs enhanced by 4KAgent are shown the right.
41

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Qualitative Comparison Fig. 15 shows qualitative results of applying 4KAgent to various base models under identical prompts. Across different models, 4KAgent consistently enhances visual fidelity and preserves fine-grained details. As shown in Fig. 16, images upscaled from SANA-IK using 4K Agent exhibit richer textures and stronger aesthetic alignment than those generated natively at 4K resolution by SANA-4K.
SANA-4K
SANA-1K 4KAgent
Figure 16: Visual comparison of aesthetic preference alignment between SANA-4K and SANA- 1K+4KAgent using identical prompts sampled from GenAlBench-4K. SANA-IK+4KApe KK+AKgt yields superior aesthetic alignment and richer high-resolution details, highlighted in the zoomed-in patches.
Discussions The application of our Agegt in AIGC scenarios leads to substantial improvements in image quality when upscaling IK-resolution images to 4K. First, when applied to IK-resolution in- puts, 4KAgent consistently achieves notable gains across multiple quantitative benchmarks, enabling more detailed and accurate reconstructions in the resulting 4K outputs. As traditional metrics are not specifically tuned for ultra-high-resolutee enable srccetuuallyfoousee evaluation. The results indicate that 4K-uscade images achieve per- ceptual quality scores comparable to their original 1K counterparts. Second, 4KAgent demonstrates strong capability in synthesizing high-fidelity visual details and intricate textures. However, with many traditional super-resolution methods, this training-free framework occasionally introduces unin- tended bokeh-like artifacts, particularly in blurred background regions. Given 4K Agent's modular and scalable design, we believe integrating task-specific profile configurations and perceptual alignment strategies could further reduce artifacts and improve robustness in diverse AIGC applications.
images, we adopted the adaptive MUSIQ-P metric to
F Experiment Part IV: Scientific Imagery Super-Resolution
F.1 Remote Sensing Image Super-Resolutio
High-resolution satellite imagery is foundational for a wide spectrum of remote sensing tasks, including urban planning, environmental monitoring, and disaster response [64, 171]. However, due to cost, bandwidth, and sensing constraints, acquiring such high-resolution imagery globally at very high frequency remains very expensive or even impractical. Recent advances in deep learning- based super-resolution have provided promising alternative by reconstructing high-fide sigh-fidliity imagery from lower-resolution observations [92]. In this section, we evaluate 4KAgent against state-of-the-art baselines on diverse set of real-world satellite image super-resolution datasets.
Settings We evaluate our models on four benchmark datasets covering varied land-use patterns and sensing characteristics:
AID [223] is large-scale dataset constructed to benchmark aerial scene classification methods. It includes over 10,000 high-resolution aerial images across 30 scene categories, such as airports, industrial areas, and farmlands. Each image has a resolution of 600 600 with spatial resolution
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of 0.5-8 m/pixel. Images exhibit high intra-class diversity and low inter-class variation, making them well-suited for evaluating generalization in SR tasks.
DIOR [111] a comprehensive object detection benchmark in the remote sensing domain, contain- ing 23,463 images and 192,472 annotated object instances across 20 categories. The resolution of images is 800 800, and the spatial resolutions range from 0.5m 30m. These images exhibit high diversity in resolution, imaging conditions, and object scale.
acrial images collected from various sensors. It features over 188,000 labeled object instances with arbitrary orientations. Each image is in resolution about 4000 The combination of large-scale, fine-grained annotations and high inter-scene variability makes DOTA particularly valuable for evaluating pereeptual fidelity.
DOTA [222] consists of 2,806 altra-bigh-resolute
WorldStrat [37] is a unique dataset designed for real-world satellite image super-resolution tasks, with globally stratified land use coverage across 10,000 km² It pairs 1054 1054 pixel high- resolution (SPOT 6/7, 1.5 m/pixel) and temporally matched low-resolution (Sentinel-2, 10 m/pixel) imagery for thousands of regions worldwide. Importantly, unlike synthetic degradation benchmarks, low-resolution (LRR) and high-resolution (HR) image pairs, introducing natural misalignment and color mismatches due to different sensor characteristics.
WorldStrat contains real cross-sensor-captee
From each dataset, we select 100-200 representative scenes. Following [227, 228], for AID, DIOR, and DOTA, HR images are downsampled using bicubic interpolation to generate corresponding LR inputs. For WorldStrat, we adopt the datasets official pre- pre-proeessing pipeline, selecting LR images that are temporally closest to each HR acquisition. Notably, due to the different sensors used for LR and HR captures, RGB content may exhibit significant variation, posing a realistic challenge for super-resolution. To test the generalization ability of our models, we evaluate on a spectrum of resolution scales: 1) 4x SR (128 +512) on DIOR and DOTA datasets; 2) 4x SR (160 (640) on AID and WorldStrat datasets; 3) 4x SR +2048 for high-res DOTA scenes; 4) 16x SR (e.g.. 256++4096 for DOTA scenes.
We evaluate 4Kggent with the AerSR-s4-F profile and AerSR-s4-P profile 4x super resolution for Fidelity and Perception preference, respectively. Then we evaluate 4KAgent with the Aer4K- profile and Aer4K-P profile in 16x super resolution for Fidelity and Perception preference, respectively. We benchmark 4KAgent against the following categories of SR models: 1) Expert aerial SR models: HAUNet [196), TransENet [102]; 2) Fidelity- SR models: HAT-L [29], PiSA-SR- PSNR [179], and SwinlR [119]; 3) Perception-based SR Models: DiABBIR [123], OSEDiff [216), HAT-GAN [29]. PiSA-SR [179], and SwinIR (Real-ISR) [119]. For PPSA-SR SEAASR-PSNEE we set the pixel guidance factor Xpiz 1.0 and semantic guidance factor Asem 0 for PiSA-SR [179] in inference. Additionally, we include AgenticIR for agentic system comparison. Together, these diverse datasets and models enable comprehensive evaluation of 4Kggent in terms of both pixel fidelity and perceptual quality, across synthetic and real-world degradation settings.
Quantitative Comparison We report the comparison results on AID (4x SR, 160-+640), DIOR (4x SR, 128-+51),, DOTA (4x SR, 128-+512) WorldStrat (4x SR, 160->440), DOTA (4x SR, 512-+2048), and DOTA (16x 4K SR) in Tabs. 16 21, respectively. Across all six benchmark settings, 4KAgent with Fidelity preference consistently demonstrates superior performance in terms of pixel-level reconstruction. It ranks within the top three PSNR in four out of six tasks, and ranks within the top three in SSIM across all synthetic scenarios. This confirms its ability to preserve structural details across scales and domains. Importantly, 4KAgent also consistently outperforms AgenticIR by large margin across all fidelity metrics and tasks, highlighting its effectiveness.
In terms of perceptual quality, 4KAgent with Perception preference achieves top performance in perceptual quality assessment metrics across multiple datasets. Notably, on the WorldStrat, 4K Agent (AerSR-s4-P) ranks first in MUSIQ, MANIQA, and CLIPIQA. These results indicate that 4KAgent not only produces photorealistic outputs but also maintains robustness in real-world, sensor-min maiso--miallgened scenarios. Compared to AgenticIR, 4KAgent with Perception preference shows clear gains across all perceptual IQA metrics, reaffirming the value of 4KAgent in balancing realism and structure across diverse and challenging remote sensing settings.
Qualitative Comparison Figs. 17 to 21 present comprehensive visual comparison of all evaluated models across the tested datasets of 4x AID, 4x DIOR, 4x DOTA, 4x WorldStrat, and 16x DOTA. Firstly, 4KAgent with the perception preference consistently delivers superior perceptual quality on
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Table 16: 4x performance comparison of evaluated models the AID dataset (160 (660~>64). The top three results for each metric are marked in bold, underline, and italic.
DataSet
PSNRT
SSIM1
LPIPS)
FIDL
NIQEL
MUSIQU
MANIQAT
CLIPIQAT
SwiaIR [119]
28.4857
0,7422
0,1355
0.2473
186.
7.1295
50.1222
0.3108
0.2563
Fidelity
baseltt-basedda based SR
HAT-L [291
27.1630
0.6835
0.4635
0.2330
126.9911
7.3586
36,4299
0.4149
0.4111
PISA-SR-PSNK [179]
27.90779
0.7251
0.4233
0,4273
144,3109
7.2571
41,6239
0,4024
0.1831
SwinIR (Real-ISR) [119)
26.5090
0.3344
129.3879
0.5205
[29]
25.7860
0.6643
140.2364
0.3556
Perception based
DurBIR [123]
24,8343
130.6386
63,96446
0.7302
OSEDif [216]
25 222
0.6164
0,3497
0.3497
91,5957
3,6661
63,9855
0.6219
0,6074
PISA-SR [179)
24.5971
0,5903
0.354
0.3541
115.8287
3,4859
66,0433
0.6855
0.6346
Expert Aerial SR
HAUNet [196]
28.5136
0.7146
0.4327
0.2083
122.1656
7,2197
35.5021
0.4162
0.1706
TranseNet [102]
28.0317
0.6983
0.479
0.2109
125,9495
6,7700
35.1140
0.3776
0.1162
AgeatieIR [274]
21.3431
0.5147
0,4600
0.2539
149,7191
4.5325
67.0257
0.6283
0.6693
Agentic System
Agentt (AerSR-44-F)
28.5481
0.7157
0.4436
0.2263
127.4411
7.9916
37,5711
0.3774
0.4322
4KAAgent -4-rRR--4-P)
24.4212
0.5354
0.4696
0.2566
139.7775
4.8915
68.1858
0.6439
0.6897
Table 17: 4x performance comparison of evaluated models on the DIOR dataset (128++512). The top three results for each metric are marked in bold, underline, and italic.
Type
Model
PSNR1
SSIM1
LPIPS
DISTSL
FIDL
NIQE,
MUSIQQ
MANIQA1
CLIPIQA!
SwintR [119]
27,8751
0.7257
223.4653
7.1247
$1.6481
0,3122
0.2441
Fidelity-bused SR
HAT-L
27,7555
0.6962
0,4586
0.2195
134,4649
7,1142
37.1784
0,4151
0,4194
ASA-SRRPSNR [179)
27.4176
0,7118
0.4378
0.2202
167.7604
6.9455
42,5087
0.4095
0.1881
SwinIR (Real-1SR) [119]
26/4708
0.6698
0.3391
0.1983
144,3900
3.8921
60.6319
0.5552
0.5091
HAT-GAN (29]
26.8015
B.6848
0.33%
0.2073
149.8121
4,8459
55. 8148
0,5992
0.3392
Poreeptioe-bused
DiATBR [123]
0,5742
146.2642
OSEDiff [216]
25.0470
0.6207
0.3506
0.1875
127,7888
3,6641
68,3933
0.6245
0,5976
P.SA-SR [179]
24.4078
0.5932
0.3534
0.3534
129.2724
3,5111
67.6365
0,6571
0.6229
Expert Acrial SR
HAUNer [196]
27 8221
D.6992
04527
0,2100
128.8770
6.9586
35.5885
0.4089
0.1572
TransNeer [102]
27.3002
0.6824
0.4391
0,2113
129.4897
6.4985
34.7713
0.3750
0.0984
AgentclR [274]
22.4811
0.5654
0.4668
0.2388
169.2341
4.7446
63.1399
0.5938
0.6252
Agentic System
4KAgent (AerSR 54-FF
27,6761
0.7062
0.4309
0.2250
146.5618
7.2555
37.5543
0.3811
0.4368
4K.Agent (AerSR-s4-P))
24,4893
D,5795
0.4374
0.2471
160.6006
4.6522
68.0117
0.6358
0.6456
Table 18: 4x performance comparison of evaluated models on the DOTA dataset (128-5511 The top three results for each metric are marked in bold, underline, and italic.
Type
Model
PSNK1
SSIM
LPIPS)
DISTS:
RIDE
NIQE;
MUSIQU
MANIQAT CLIPIQAT
HAT-L|299
33,0720
0.1471
58,0105
51.7547
0.3725
Fidelity -aasdd--eedddee SR
ESSA-SR-SNNR [179)
28.9623
0.7999
0,3415
0,2093
133.7664
7.7350
47.4847
0.4878
0.3094
SwinIR [119]
30.5969
0,8251
0.3275
0.2215
143.2866
7.5391
51.3111
0-4526
0,2700
DiTBBR [123]
8326
B.6489
0.3724
0,2340
115.1440
6,1906
64.9539
06535
0.6772
OSEDiff [216]
26.3616
0.7156
0.3324
0,2133
126.4670
5.4257
64.1220
0.6278
0.6736
Perception-bassel
HAT-GAN [29]
28.6557
0.7869
0.2751
0.1818
115,1743
5.7245
57.0159
0.5929
0.369]
[179]
0.6921
4,9062
0.6855
SwintR (Keal-ISR) [119]
110.4552
4.7712
0,4519
HAUNNet [196]
32
0.8627
0.2480
0.1428
$7.3008
6.5917
50.7492
0.5711
0.3824
Expert Aerial SR
TransENot [102]
30.7214
0.8176
0.2883
0.1553
68.5122
6.2878
42,8957
0.4856
0,344
AgenicIR [274]
19.9655
137.2777
63.5596
0.6198
Agentic System
4Kegget (AerSR-44-8)
31,3589
0.2853
88.0366
0.3799
4KAAenn (AerSR.s4.P)
24.9224
0.6427
0.3884
0.2555
131,0346
6.1609
67.0355
0.6701
0,6800
low-resolution SR datasets, as demonstrated in Figs. 17 to 20. In contrast, 4KAgent with the fidelity preference excels on high-resolution 4K SR datasets, producing the most faithful reconstructions in Fig. 21. Secondly, 4KAgent exhibits a clear advantage in reconstructing fine structures such as lines and patterns, as evident Figs. 18 and 19. Finally, the challenging cross-sensor super-resolution scenario of WorldStrat, where LR and HR images originate from different sensors. 4KAgent with
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Table 19: 4x performance comparison of evaluated models the WorldStrat dataset (160~>640). The top three results for each metric are marked in bold, underline, and italic.
Type
PSNR1
SSIM-
DISTS)
FIDJ
NIQEL
MUSIQU
MANIQAT
CLIPIQAT
HAT-L [29]
21.2238
0.6480
0.3468
022088
145.3798
9.0026
30,6655
0.2844
0.2539
Fidelity-busee based SR
PiSA-SR SR-PSNRRRRRR------ [179]
24.4312
0.7271
0.3312
0.2283
142
10.5327
29.6462
0.2994
0.2509
SwinlR [119]
18.0957
0.6136
0.3451
0.2279
180.4954
8.2607
27.1954
0,2104
0,2555
DifiBRR [123]
20,6485
OSEDif [216]
25.9716
0.6316
Perception based
HAT-GAN [29]
27.0796
0.7241
[47,4441
0.2623
PISA-SK [179]
23.9304
0.6179
0.4581
0.2748
170.0426
7.2214
48.6414
0.5152
0,5010
SwinlR (Real-ISR) [119]
27.9062
0.7120
0.3473
0,2077
149 4428
10.1237
32,9484
0.3497
0,3060
Expert Aerial SR
HAUNet [196]
26.1895
0.7143
0.3141
0.1976
128.2747
10.6318
28.4401
0.3129
0.2701
TransNNet [102]
24.4879
0.6944
0.3270
0.2106
133 6939
7.7765
27.8965
0.3152
0.2246
AgenticIR [274]
19,5883
0.5188
0.6716
0.3686
224.8042
8.7079
$4,0649
0.5166
0,5402
Agentic System
4K Agent RAerSR-s4-FF)
22.3529
0.6470
0.3702
0.2324
166.2731
9.5364
34,3698
0.3011
0.2875
4KAggent -4-FR
20.1510
0.5379
0.6363
0.3664
223.4866
8.5528
56.8421
0.5547
0.6236
Table 20: 4x performance comparison of evaluated models on the DOTA dataset (512~>204)). The top three results for each metric are marked in bold, underline, and italic.
Type
Model
PSNR1
SSIM
LPIPS!
DISTS
FID
NIQE,
MUSIQ1
MANIQAT
CLIPIQAT
HAT-L[29)
38.4856
0.9101
0.1956
0.1007
0.7222
6,8076
39.5562
0.5006
0,3408
Fidelity-based
PISA-SR-PSNR (179)
0.8519
0.3183
0.1797
40.7879
7,0981
38.6277
43.3724
DiABBR [123]
26.3032
0.6490
0,4035
0.1953
76.3522
4.0360
$7,2133
0.6265
0.7306
OSEDIT [216]
28.5768
0.7567
0,3632
0,2127
70.4222
4,1286
51.4468
0.5871
0.6907
Pereeption based SR
HAT-GAN (29)
31.2735
0.8408
0.2720
0.1527
47,0606
4.6791
47. 47.6011
0.5508
0,3551
PSSA-SR [179]
27,6765
0.7303
0.3499
0.2175
54,5844
3,8036
51,7604
0.6135
0.6353
SwinIR (Real-1SR) [119]]
31,6933
0.8423
0.2564
0.1453
37,3714
4,1055
48.3376
0.5561
0,4088
HAUNet [196]
38.2237
0.9075
0,2002
0.0974
0.2984
6.6907
38.8776
0.4926
0.3471
Expert
TranENNer [102]
35,9776
0.8824
0,2431
0.1267
0.2137
6.3886
34,8775
0.4345
0,2942
AgenticlR [274]
21,4719
0.7284
0,4157
0.3167
77.7286
4,7902
49.5913
0.5496
Aganti System
4KAgent (AerSR=s4-F)
0.234
0.1283
11.0017
7.0361
38.7286
4KAgent (AerSRR-4-PP)
0.3410
41.1425
3,9267
$2.1735
Table 21: 16x performance comparison of evaluated models the DOTA dataset (4K resolution). The top three results for each metric are marked in bold, underline, and italic.
Type
PSNRT
SSIM
LPIPS.
DISTSL
FIDE
NIQEJ
MUSIQU
MANIQAT
CLIPIQAT
HAT-L-(2))
23.9586
B.6362
0.6471
0.3219
82.7644
9.08077
31,5394
0.2565
0,3062
Fidelity-busedd SR
SSS--RR-PNK [179]
22,6265
0.5994
0.7279
0.3368
110.5112
9.2583
24.0154
0.2066
0.1766
SwintR [119]
22,9425
0.6095
0.6860
0.3815
162.1128
9.7613
37.0268
0.2792
0,2814
DiiBBR
21,4093
114.8595
57.5771
0.5030
0.7588
OSEDDIff [216]
22.0602
0.5544
0,2647
107.3622
4,1667
0.7287
Penespiioe-bused SR
HAT-GAN (29]
21.7525
0.5901
0.5590
0.2668
139.2047
5,4465
45.6411
0.2791
0.3448
PSSA-SR [179]
22,1022
0.5761
0.5552
0.2517
100.3336
4.2723
48.0151
0.3191
0,5972
SwinIR (Real-ISR) [119]
6770
0.5731
0.5431
0.2431
129.7745
3.7477
50.5413
0.3033
0,48K5
Expert Aerial SR
HAUNet [196]
23 6649
D.6268
0.6922
0,3304
86.2487
9.0018
26.2489
0.2207
0.2567
TrunsENer [102]
22.9690
0.1765
AgenticIR [274]
17.8736
D.4675
0.5928
0.2451
135.6437
3,8950
$4,3685
0.4301
0.6551
Agentic System
4KAgent (Aer4K-F))
23.4348
0.6255
0.6520
0.3312
105.6710
9.0064
33.6645
0.2725
0.3314
4kAgent (Aer4K-P)
21 11.9826
0.5515
0.3325
0.2415
112,2518
3.7330
55.7730
0.5175
0.7159
the perception preference still maintains promising visual performance, as shown in Fig. 20. This demonstrates the robustness of 4KAgent across both resolution scales and sensor domains.
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LEC EEE
HATL
FIA 52 PANR
OSEENI
BAFGAN
PSA SR
EER
Publig prtigg -1
SWAER JANl 253)
RAINV
TecdENe
Agras-I2
IK IKAggee (Se15R of AKAgeee (ArISR
Grooal Tirb
HARSL
PRAA-SL-SNNE
HARFGAN
PISASK
Sadiea wwdau. 220
SuazR 1SR)
RAUNe
TeicEENi
Agensdi
4KAgger (AuIRR +4 Fy 4KAggst UsrrRR 44
Geraad Trch
BATL
RSA SK. PANR
Swialk
O6RDNT
HAT
PiSs SX
Aguizik
4kAgcmi (harSB--6-))) 40cApeat (Auriil----77
Figure 17: Visual comparison the AID dataset (160~6440)
4K-Agner KKAeg
FISA-RR
AparkiR
4KAgrat
dKAggat (loeSR
I
AgeeTCIR
4KAAgont 4KAgpat
Figure 18: Visual comparison on the DIOR dataset (128-+512).
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(SR)
TocdEe
AgraiviR
(K
BATL
PISA-S0-PSNK
SnaaR
OSEENE
BAT-GAN
2:AA-88
SWiKER (501
RAINN
AgraE-ER
4K IKAgger (SASSR 41 IK Sigee (AWISR
Cloweol Trrhh
RARL
OSTZNE
HATSGAN
PISA-IX
PY222
Swidck (Reel (S3)
BAUNNe
Townsher
AgretVER
4KC Agnet (AvVSR 44 dKAkgrat (bveSR +4
Goraal Trch
Figure 19: Visual comparison on DOTA dataset (128++112).
Agentiik
4KAAgeni +4-8 4KAggal
Figure 20: Visual comparison the WorldStrat dataset (160~644)).
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HARE
P. P-AABRAEEEE
SAAB
DATEER
OSETAE
BASGAN
TSAGR
Suiik: (Rai-E8R)
BAIINE
AQREEI
Goeal Trak
SwieR
CXSTAUT
BATOAN
TSA-RR
FAC1
Swich (58)
HALIN
InastNet
Apewii
Oneal
0
IATTL
FEA-S1PCM
twelll
DARII
DISDIT
BATGGA
MAASI
PE3155
Seadll (Raak3RR)
TraaXSa
(AurlcC))
Figure 21: Visual comparison on the DOTA dataset (4K upscaling).
Discussions These results across fidelity and perception metrics, combined with qualitative visual comparisons, provide several key insights into the advantages of 4KAgent. First, the consistent top-tier performance of 4K Agent with fidelity-based profile across wide range of datasets and scaling factors suggests that the agent's analytical pipeline and adaptive control provide more precise reconstruction than traditional feedforward models. Unlike conventional SR networks that are typically optimized for either low-level fidelity or high-level realism, 4K 4Kggents architecture decouples these objectives through specialized profiles, allowing excel both domains without compromise. Second, the perceptual strength of 4KAgent is reflected not only in numerical scores but also in its sharper textures, reduced artifacts, and more semantically coherent outputs in qualitative results, demonstrating the value of integrating agentic reasoning with perceptual priors, especially in real-world datasets like WorldStrat. Finally, the margin by which both 4KAgent variants outperform AgenticIR, demonstrating the superiority of 4KAgent in terms of agentic system. Our contributions the design of specialized profiles, adaptive modulation, and perceptual alignment mechanisms are crucial to bridging the gap between task generality and SR specialization. Together, these findings indicate that agentic architectures, when properly aligned with SR objectives, enable scalable, generalizable, and controllable super-resolution across diverse remote sensing domains.
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F.2 Fluorescence Microscopic Image Super-Resolution
Confocal fluorescence microscopy is one of the most accessible and widely used techniques for studying cellular and subcellular structures [67, 235]. It builds sharp image by using either single pinhole to sean point-by-point or an array of pinholes on a spinning disk to sean multiple points simultancously to reject out-of-focus light, offering molecular specificity and 3D sectioning capabilities. However, is constrained by diffraction-
resolution down to 200 nm under visible light [184]. Meanwhile, high-intensity illumination required for improved resolution leads to photobleaching and phototoxicity, limiting live-cell imaging duration and data throughput [185]. Deep learning-base based fingle-imae super- resolution (SISR) methods have shown great promise in recovering high-frequency details from lower-resolution inputs in biological microscopy, overcoming some limitations of hardware-basee SR techniques [139], despite the scarcity of large, publicly available fluorescence microscopy datasets. To extend the evaluation of our 4K Agent on this scientific application of different modalities, we conducted experiments on representative dataset against baselines from major SISR families.
Settings We evaluate 4KAgent on SR-CACO-2 benchmark dataset [12], which contains 2,200 unique images of the Caco-2 human epithelial cell line, labeled with three distinct fluorescent markers: Survivin (CELLO), E-cadherin / Tubulin (CELLI), and Histone H2B (CELL2), at x2 (256-5112), x4 (128 +512), and x8 (64-512)) scales. To generate high- righrresouution images, each tile was scanned with a 1024x 1024 pixel resolution, and 8 scans were captured and then averaged together reduce noise. Meanwhile, low-resolution images were captured directly by the microscope at three different scales without averaging. The full dataset contains 9,937 patches for each cell extracted from scanning confocal volumes, with tiles 9, 10, 14, 20 used the test set. In experiments, we randomly sampled 100 patches from each marker category in the test set at three super-resolution scales.
We evaluate 4Kggett with the ExpSR-s2-F profile, ExpSR-s4-F profile, and ExpSR-s8-F profile, considering the demands and requirements of the microscopy image super-resolution task. We benchmark 4KAgent against 15 representative SISR models, broadly spanning pre-upsampling, post-upsampling, iterative up-and-down sampling and progressive upsampling SR methods. Each model has been trained on the SR-CACO-2 training set before deployment. Encompassing a wide spectrum of upsampling strategies, this rigorous benchmark ensures a comprehensive comparison between our 4KAgent and other specialized and general-purpose purpose SR methods, assessing 4KAgent's blind inference performance on the novel microscopy data domain.
Quantitative Comparison All quantitative results are in Tab. 22. PSNR, SSIM, and NRMSE were selected as criteria. We noticed that the background of cell constituted a significant proportion of an image, as was also reported in the original SR-CACO-2 benchmark. Because including the non- informative dark background in evaluation can lead to inflated and biased performance metries, we adopted the masking strategy described in [12] to define our Regions- onfennteest (ROIs) and calculated performance metrics based only on these areas. Across different scales and cell types, 4KAgent with Fidelity preference consistently achieves top performance in pixel-level reconstruction in ROL. The superior result on PSNR, SSIM, and NRMSE confirms Agent''s effectiveness in reconstructing fine fluorescence-labele structures and low-level pixel fidelity, under various downsampling conditions. Furthermore, when compared with ENLCN, one of the most competitive methods, our 4KAgent with fidelity mode consistently exhibits clear advantage in all numerical metrics, underscoring its ability to handle the blind super-resolution task for real-world microscopy data.
Qualitative Comparison Representative qualitative results for the highly challenging 8x super- resolution task are shown in Fig. 22. At such high magnification, where information loss is severe, the ability to reconstruct distinct biological structures for each of the three cellular markers becomes a critical test for any SISR method.
Our visual analysis reveals clear performance differences. For the inherently dim and sparse CELLO Survivin marker, 4KAgents reconstruction is markedly clearer and closer to the ground truth. It successfully restores the faint midbody structure with higher fidelity than top-performing baselines like ENLCN and ACT, which struggle to resolve this signal from the background. This superior performance is also evident for CFLLII where 4KAgent delineates the membrane and cytoskeletal framework with sharp, continuous lines. In contrast, the outputs from most other methods appear
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Table 22: Performance comparison of evaluated models the selected sr-caco-2 test set on ROI only, L.e, cells. The top three results for each metric are marked in bold, underline, and italic.
PSNK
NRMSEL
SSIM
SISR Methods
Scale
CECLO
CE121
CELLZ
Mean
CE1L0
[CELLL
CELL2
Mean
CELC0
CCEL1
CBLL2
Misan
34,95
32.61
30.24
32.59
0.0887
0.0681
0.0061
0.0745
0.7899
0.7836
0,7038
0,7588
Bicubic
X4
25.08
31.99
30.43
32.50
0,0793
0.0699
0.0641
0.0708
0.5411
0.7998
0.7718
0.8042
32.01
26.77
26.27
0.3311
0.1071
1.1340
0.1207
0.6808
0.6922
Pre-upaampingg SR
37.04
34.47
33.03
34,85
0.0610
0.0516
0,0471
0.0532
0.5733
0.8566
0.8283
0.8527
SRCNN [45)
35.39
52:73
31.48
33.20
0.0704
0.0610
0,0563
0.0626
0.5707
0.8104
0.8335
X8
12.52
29.16
26.53
29.41
0.1074
0.0924
0.1143
0.1047
0.8117
0.7219
0.7207
0.7514
37.50
54.29
33.00
0.0662
0.0554
0.0545
VDSR (87)
X4
16.18
32.50
31.44
33.58
0.0663
0.0638
0.0571
0.0624
0.8777
0.8218
0.8180
0.8392
X8
32.05
28.30
26.42
29.08
0.1307
0.1062
0.1237
0.1202
0.7291
0.6712
0.6877
0,6960
37.35
34:23
33.05
07609
0,0555
0.0175
000546
0.8917
0.8597
0,8386
0.8657
DRRN [180]
X4
26,00
32:50
31.43
33.31
0.0678
0.0637
0.0570
0.0628
0.5772
0.8216
0.8167
0.8385
31.92
28.31
26.39
28.85
0.1310
0.1096
0.1230
0.1212
0.7290
0,6583
0.6860
0
X2
35.69
$5.40
30.81
33.50
0.0776
0.0557
0.0607
0.0647
0.5395
0.8287
0.7759
0.811
MemNer [181]
x4
31,61
32.18
30.26
32.45
0.0808
0.0610
0.0654
0.0690
0.5465
0,8067
0.7651
0.8061
32.00
28.76
26.52
0.1183
0.7528
0.6972
0.7102
0.7201
Post-upsampling SR
37.57
34.31
33.14
35,00
0,0588
0.0527
0.0465
0.0627
0.8911
0,8563
0.8375
0,8616
NLSN
X4
26.39
32.75
31.08
33.61
0.0630
0.0600
0.0548
0.0593
0.5254
0:8179
0,8131
0.8355
X8
32.55
29.13
26.30
29.53
0.1147
0.0999
0.1205
0.1097
0.790
0.2092
0.0089
0,7880
37.21
34.20
31.74
34.72
0.0014
0.0561
0.0495
0.0550
0.3990
0.80033
0.8375
0.80.20
DRCAN [164]
35.92
32.49
31.29
33.23
0.0684
0.0653
0.0582
0.0640
0.57700
0.8233
0.8174
0.8.39
XB
11.25
28:15
25:45
28.28
0.1344
0.1079
0.1276
0.1238
0.7447
0.6749
0.6841
7.1012
24.55
34.48
0.2349
0.0527
0.0473
911110
0.3585
0.8626
0.8385
SwinlR [119]
X4
35.95
32.66
31.57
33.39
0,0673
0.0618
0:0559
0.0617
0.5772
0.8198
0.8161
0.8377
11.34
28.43
25
28.54
0.1314
0.1035
1.33
6,1190
0.7516
0.6838
0.4923
0,7092
37.59
34.41
33.15
35,05
0.0574
0.402
00011
0.5376
0.8567
0,5340
0.8595
ENECN [220]
X4
36.30
32.74
31.63
33.56
0,0638
0.0606
0.0553
0.0399
0:3766
0.8196
0.81448
0.8370
32.69
29.28
26.31
29.43
0.1108
0.0921
0.1205
0,1078
0.7998
0.7109
0.698
0,7364
X2
31.28
34:34
32.81
32.85
0.1088
0.0522
0.0122
0.070
0.3043
0.8625
0.8337
0.8355
(114]
X4
25.76
32.81
31.48
33.35
0.0678
0.0581
0,0565
0.0608
0.5774
0.8133
0.8144
0,8350
28:93
0.0953
0.1104
0.1204
0.72%
0.7110
0,7197
X2
37.24
$4.00
33.14
33.01
0,0019
0.6896
3.0159
0.0025
0.3390
0.8004
0.3288
0.85944
ACT [239]
x4
36.17
32.76
31.56
33.50
0,0622
0.0590
0.0554
0.0099
0.376]
0.8134
0.3065
0.8320
x8
32.74
29:13
26.39
29.42
0.3064
0.0915
0.1152
0.1044
0.0083
0.7128
0,7063
X2
37.35
34.19
33.02
34.85
0.0597
0,0548
0.0475
0.0840
0.5896
0,8562
0,8370
0,8609
Omni- SR [193]
35.86
32:53
31.29
0.0000
0.0635
0.0563
0.0626
0.8117
x8
30.44
28:31
25.32
37.99
0.1418
0.1075
11335
0.1255
0.7231
0.6673
0,6808
0.6904
lterative up-ond-down sampling SR
37.44
34.54
33.02
33,00
0.0588
0.0512
0.0470
(0.022
0.8872
0.8603
0,8239
0.8604
DBPN [61]
x4
36.22
32.85
31.64
33.557
0.0633
0.0591
8.0054
0.0895
0.8745
0.8216
0.8091
0.8351
32.39
28.89
26.36
29.21
0.1103
0.0946
0.1157
0,7069
0.3060
0,7084
0,7149
0,7481
36.02
33.49
31.38
33.63
0.0767
0.0611
0.0576
0.0651
0.3119
0.8205
0.7592
0.8058
SRIBNN [1177
X4 35.66
32.49
31.05
33.07
0.0729
0.0636
0.0592
0.0653
0.8589
0.8131
0.7921
0,8214
32.33
29.05
26.62
29.33
0.1243
0.1019
0.1148
0.7553
0.6809
0,7168
Prugresivv upsampling SR
X2
36.92
34.66
32,80
34.79
0.0K21
0.0494
0.0485
0.0333
0.5879
0.8577
0.8385
0.8614
ProSR [203]
X4
36.11
32.71
31.61
33.45
0,0656
0,0614
0.0609
0.8771
0.8217
0,8164
0.8384
X8
32.13
29.43
26.36
29.51
0.1268
0.0909
0.1224
0.1133
0.7504
0,7200
0.6019
0,7208
32.75
$3.49
28.
31.19
0.1014
0.0593
0.0804
0.7735
0.79%6
MS-LapSRN (97]
X4
30.91
31.36
30.69
30.99
0.1118
0.0672
0.0611
0.0801
0.8124
0.7820
0.7858
0,7934
X8
30.67
27.64
24.68
27.66
0.1206
0.1056
0.1305
0.1189
0.7829
0.6902
0.6649
0.7127
Agentic System
x2
39.92
36.95
33.95
36.94
0.0508
0.0337
0.0436
0.0434
0.9331
0.9105
0.8745
0.9087
4KAgent (ExpSR==N-F) 4,80
41.25
36.36
35.07
37.73
0.0089
0,0318
0.0066
0.0358
0.9555
0.9314
0.9089
0.9319
38.95
33.66
31.99
34.86
0.0532
0.0488
0.0002
0.0539
0.5378
0.9033
0,8929
0.9113
noticeably blurry, failing to preserve the cells essential structural integrity. In the case of the bright nuclear marker CELL2, the diffuse nature of the chromatin structure means even the ground truth image itself lacks hard, dell-defined edges. In this difficult context, 4KAgent reconstructs complex, high-frequency textural pattern that is visually competitive with the other methods. While the intricate nature of the target makes absolute fidelity hard to judge, our method effectively generates a detailed result on par with other models, even under the zero-shot blind inference setting.
Diseussions Our experiments show that 4KAgent delivers leading performance on the challenging SR-CACO-2 dataset, with quantitative metrics and qualitative results surpassing those of the evalu- ated specialist models. The superior performance of 4KAgent underscores its strong applicability to fluorescence microscopy SISR. First, it showcases strong zero-shot generalization, achieving highly competitive super-resolution performance on microscopy data, and could be further strengthened by adapting more domain-speciffe tools. Second, 4K 4KAgent exhibits impressive cross-domain transferabil- ity, successfully adapting methods originally optimized for natural scenes to the distinct characteristics of fluorescence microscopy images. Third, the agent-based architecture enables the flexible and
50

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SWILR
10 127 2045 2688 7356 55% CELLO
Uriii SR
D8PN
SRFAN
ProSit
MISaapiiN
JKAgear
38 1506 2176 2304 2044 CELLI
Omii-SR
DEPN
SRFBIN
PreSit
MSLaySRN
GT
SRONN
VI3SR
MantNer
DECAN
SwitIR
EMILEN
ACT
85%h CEL12
MSLapsRNN
dkAgee (4s
GY
Figure 22: Visualization of fluorescence microscopy image SR on SR-CACO-2 dataset (64->112).
modular integration of existing models without requiring expensive retraining or model modification. Beyond immediate applications super-resolution, the modularity and domain-agnostic nature of AKegett also suggest its broad potential for other real-world biomedical imaging domains where data scarcity or retraining costs limiting factors.
F.3 Pathology Image Super-Resolute
Pathology images, particularly whole-slide images (WSIs) and their extracted patches, play critical role in digital diagnostics and disease detection. Typically, glass slides containing tissue sections stained with hematoxylin and eosin digitized using high-speed scanners resolutions approaching ~0.25 pm per pixel, resulting in gigapixel-scale images characterized by distinct color profiles and fieffrreeency textures unique cellular structures. However, the substantial costs and data storage requirements associated with hiira-highhresolution scanning have led many workflows to rely on computational upscaling from lower-resolution acquisitions. This task is challenging, as pathology images possess specialized characteristics that pose significant difficulties for conventional single- image super-resolution (SISR) methods originally optimized for natural scenes. To address this challenge, we evaluate our 4KAgent on the beSR dataset [77], comparing it against several established techniques assess effectiveness this specialized domain.
Settings Our evaluation is conducted on the beSR benchmark dataset curated for pathology image super-resolution. The beSR dataset was derived from the larger CAMELYON [126] dataset, which contains WSIs of H&E-sttined breast cancer sentinel lymph node sections. To create bcSR, the authors first sampled representative 1024x x 1024 patches from the original WSIs. Subsequently, filtering process was applied to remove patches with large blank areas and to select for images with high color channel variance, ensuring the dataset was rich in informative and challenging tissue structures. The final bcSR dataset consists of 1, 1,200 unique images, which were split into 1,000-image training set and 200-image test set.
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Following the standard protocol established by the beSR benchmark, the high-resolution ground truth images were downsampled using bicubic interpolation to generate the low-resolution inputs. We evaluated 4K Agett using the ExpSR-s4-F and ExpSR-s8-F profiles for the 4x and 8x tasks, respec- tively, prioritizing pixel fidelity which is critical for preserving fine diagnostic details. Performance was measured using the PSNR and SSIM metrics.
Quantitative Comparison Quantitative results for the pathology image super-resolution task are summarized in Tab. 23. Across both 4x and 8x upsampling tasks, our 4KAgent achieves the highest SSIM score among all evaluated methods. While CARN. model specifically designed for this pathology dataset, attains a marginally higher Peak PSNR, 4KAgent's superior SSIM is more indicative of its ability to accurately preserve the complex tissue morphology and textures that are essential for pathological diagnosis, which is more critical for the reliability of features used for clinical assessment. This demonstrates the effectiveness of 4KAgent in recovering diagnostically relevant details from downsampled pathology patches, with performance comparable superior to other specialized, fully-trained models.
Table 23: Quantitative comparison on Pathology dataset. The top three results for each metric are
marked in bold, underline, and italic.
Method
4x
8x
PSNR
SSIM
PSNR1
SSIMT
Ricabic
27.019
0.6659
22.475
D.2776
SRCNN [45]
27.475
0.7329
22.489
0.3624
SRGAN [98]
28.606
0,7719
23.729
0,5580
EDSR [121)
29.830
0,8058
24,366
0
RDN [267]
29.913
0.8074
24.392
0,5711
RCAN [266]
29.916
0. 8085
24. 44.404
0.5749
SWD-Net [32]
29,853
0,8000
24,165
0,5755
CARN (77)
29.964
0.8408
24.479
D.5763
4KAgent (ExpSR IN e (4, a1)
29.746
0.8602
24.300
0.5826
Qualitative Comparison Fig. 23 presents qualitative comparison for 4x super-resolution on representative patches from the beSR test set. While both methods significantly improve upon the heavily blurred low-resolution inputs, a closer inspection of the ROIs reveals that our training-free 4KAgent consistently produces results that are on par with, and often superior to, the fully-trained, domain-specift CARN model. This superior performance particularly evident in challenging cases. In image 1010, 4KAgent successfully delineates individual cell boundaries and restores the heterogeneous texture of the tissue architecture. In contrast, CARN's output suffers from loss of sharpness and definition, resulting in noisier and blurrier cell regions and poorly defined tissue architecture, while also introducing a slight color deviation. Similarly, image 1175, 4K Agent accurately reconstructs the intricate internal structures, preserving the sharp outlines of the nuclei and cytoplasm. CARN's output, conversely, suffers from a loss of sharpness and inaccurate detail, while also exhibiting subtle grid-like artifacts. Across all examples, 4KAgent consistently generates nuclei with sharper boundaries and more distinct internal textures, along with clearer cell membranes, demonstrating higher fidelity to the ground truth.
These visual improvements also correlate with higher SSIM scores of 4KAgent, confirming its enhanced ability to preserve the structural integrity of the tissue. The accurate recovery of such fine- grained morphological details is critical for potential downstream clinical applications. Hfgh-fidelity reconstructions like those from 4KAgent can enable more reliable automated analysis, such as precise nuclei segmentation for cell counting, classification of cellular atypia, and grading of cancerous tissue, thereby highlighting the potential value of our approach in digital pathology workflows.
Discussions The combined quantitative and qualitative results underscore the significant potential of 4KAgent for pathology image super-resolution. Although not leading in PSNR, 4KAgent's s superior SSIM scores demonstrate a more accurate reconstruction of high-frequency textures and tissue morphology, which are paramount for pathological interpretation. Furthermore, because 4K Agegt not trained on a specific pathology dataset, it is less susceptible to overfitting to the characteristics of single data source. This provides a significant advantage when performing SR
52

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1010
LR
CARN
4KAgent (ExpSR-s4-F)
GT
1057
LR
CARN
4KAgent (ExpSR-s4-F)
GT
1123
LR
CARN
4KAgent (ExpSR-s4-F)
GT
1175
LR
CARN
4KAgent (ExpSR-s4-FF
GT
Figure 23: Visual comparison of pathology image super-resolution bcSR dataset (256 1024).
on real-world pathology images acquired by different scanners and staining protocols. Additionally, 4KAgent's agentic framework allows for flexible expansion of its tool profile better adapt pathology imaging modalities. Its leading performance on the bcSR dataset validates the potential of this agentic approach as robust and generalizable solution for biomedical imaging.
The ability to generate reconstructions with high structural fidelity has direct implications for critical downstream applications in computational pathology. By restoring sharper nuclei, clearer cell membranes, and more intelligible tissue architecture, 4Kgget provides a more reliable input for automated analysis pipelines. This can enhance the accuracy of tasks such nuclei segmentation, cell counting, and the classification of cancerous tissue, ultimately making it a more robust and practically useful tool for Al-assisted biomedical diagnostics.
E.4 Medical Image Super-Resolution X-Ray, Ultrasound, and Fundoscopy
In this chapter, we shift our focus to the super-resolution of clinical diagnostic imaging modalities, where the primary goal is to enhance anatomical and pathological details for improved diagnostic accuracy while minimizing patient burden, such as reducing exposure to ionizing radiation in X-ray imaging [188]. Although often grouped together, these modalities can be fundamentally diverse, operating on different physical principles: from X-rays utilizing ionizing radiation to ultrasound relying acoustic waves. This diversity gives rise to unique image characteristics and modality- specific challenges, such as maintaining pathological invariance in chest X-rays or avoiding the generation of pseudo-structures in ultrasound images.
The prevailing approach medical image SR has been the development of highly specialized models, each trained specific datasets for single modality [243]. The rise of foundation models
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has led to more powerful specialist systems, such as those tailored for single modality like CT or dermatology [157, 86], though few pioneering works have begun explore more universal solutions [122]. A significant drawback of such specialized paradigm is poor generalization across different datasets and modalities, which creates a major bottleneck for practical clinical deployment and motivates our evaluation of 4KAgent's performance on these challenges. To this end, this chapter evaluates 4KAgent's performance across several distinct and clinically important modalities.
Settings We evaluate 4KAgent with the ExpSR-s4-F profile across three medical imaging modali- ties with distinet imaging principles: X-ray, ultrasound, and fundoscopy, benchmarking against their respective baselines. Benchmark datasets of each imaging modality are summarized as follows:
X-ray. Chest X-ray 2017 [85] and Chest X-ray 14 [199]. Specifically, Chest X-ray 2017 is a dataset of 5,856 pediatric images from Guangzhou Women and Childrens Medical Centre, split into 5,232 images for training and 624 images for testing. Chest X-ray 14 contains 1 112,120 120 frontal-view X-rays of 30,805 patients with 14 disease labels mined from radiology reports. Among them, 880 images additionally contain expert-annotated bounding boxes. Following the settings in [230], we evaluate on the Chest X-ray 2017 test set and the 880 annotated data in Chest X-ray 14.
Ultrasound Image. US-Case [175] and MMUSIK [154]. The US-Case collection comprises over 7,000 sonographic images spanning organs such as the liver, heart, and mediastinum. Adopting the selection protocol from [154], we reused the subset of 111 images in the test set for benchmarking. excluding scans whose small field of view limited their diagnostic value. MMUS K features 1,023 anonymized multi-organ ultrasound scans, including bladder, gallbladder, thyroid, kidney, etc., sourced from Shanghai Tenth Peoples Hospital. All images meet a minimum resolution of 448 600 px and were cleansed of watermarks and blurring artifacts via LabelImg. The test set with label numbers from 0801 to 0900 was used for evaluation.
Fundoscopy Image. DRIVE [176] consists of 40 color fundus images from diabetic retinopathy screening program in the Netherlands collected by a Canon CR5 non-mydriatic 3CCD camera. The dataset is equally divided into 20 for training and 20 for testing. As all images are in 584x565 resolution, following the setup in [5], the original high-resolution (HR) images were resized to 512x512 and before being further utilized to generate LR pairs.
To consist with baseline methods for each dataset, the LR input images were generated by down- sampling HR images via bicubic interpolation. For X-ray images, we use SSIM, FSIM [261], and MSIM [209] as metrics, while PSNR and SSIM are used for Ultrasound and Fundoscopy images.
Table 24:
Quantitative comparison on X-ray datasets. The top three results for each metric are marked in bold, underline, and italic.
Method
Chest X-ray 2017
Chest X-ray
SSIM
FSIM
MSIM
SSIM
FSIM
MSIM
Nearest-Neighoor
0.637
0.672
0.668
0.701
0.724
0.713
Interpolation [231]
0.615
0.663
0.644
0.687
0.698
0.681
CTF [2581
0.933
0.917
0.955
0.943
ESPCN [172)
0,756
0.825
0.804
0.795
0.822
0.815
FSRCNN [47]
0.897
0.943
0.953
0.917
0.959
0.953
LapSRN [96]
0.893
0.942
0.454
0.915
0.956
0.949
SRGAN [98]
0.821
0.896
0.844
0.903
0.897
GAN CIRCLE [240]
0.897
0.947
0.923
0.919
0. 0.969
0.945
SNSRGAN [230]
0.981
0.925
0.995
0.986
4KAgent (4x))R-44FF)
0.933
0.996
0.987
0.960
0.999
0.993
Quantitative Comparison X-ray Quantitative results are summarized in Tab. 24. Ultrasound Quantitative results are summarized in Tab. 25. Fundoscopy Quantitative results are summarized in Tab. 26, which collectively demonstrate 4KAgent's consistently superior performance across all
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three distinct medical imaging modalities. On the X-ray datasets, 4KAgent with Fidelity profile surpasses the specialized SNSRGAN [230] model across all structure-focused metries, For Ultrasound imaging, it also achieves a significant performance leap, boosting the PSNR on the MMUS1K dataset by nearly 3 dB over the previous state-of-the-art, M2Trans [154]. Similarly, on the DRIVE Fundoscopy dataset, 4KA again sets a new performance benchmark, improving the PSNR from 37.72 to 41.52 and the SSIM from 0.91 to 0.95. This consistent outperformance across modalities, from the need for pathological invariance in X-rays to the clarity of fine vessels in fundoscopy, highlights the effectiveness and robustness of 4KAgent for diverse medical SR tasks.
Qualitative Comparison Representative qual- itative results for X-Ray SR, ultrasound SR, and fundoscopy SR are shown in Figs. 24 to 26. The visual outcomes generally align with our quanti- tative findings and suggest the potential benefits of 4KAgent for clinical imaging applications.
Table 25: Quantitative comparison on Ultrasound dataset. The top three results for each metric are marked in bold, underline, and italic.
Method
US-CASE
MMUSIK
PSNRT SSIM
PSNRT
SSIM
For X-ray super-resolution. where maintaining pathological invariance is important, 4KAgent produces reconstructions with improved delin- eation of lung parenchyma and clearer visibil- ity of rib cage contours. It appears to achieve this clarity while reducing some of the over- smoothing artifacts occasionally seen in other SR methods, thus helping to preserve diagnostic details that are crucial for identifying pulmonary abnormalities with greater confidence.
Bicubic
28.90
0.7892
28.24
0.7817
EDSR [121]
30.82
0.8497
30.04
0.8326
Swintk [119]
0.7834
27.66
0,7758
[263]
31.02
0.8464
80.40
0,830
[134
30.84
0.8574
30.25
0.8235
[30]
28.72
0.7812
28.08
0.7582
M2Trans [154]
31.32
0.8516
30.68
0.8392
In the ultrasound comparisons, 4KAgent shows a notable advantage. On the US-CASE example, restores clearer tissue boundaries and more internal detail compared to the blurrier recon- struction from the M2Trans baseline. Similarly, for the MMUSIK image, 4KAgent appears to reduce speckle noise while enhancing anatom- ical definition, whereas the baseline result is affected by some noise and artifacts. In both cases, 4KAgent generates echogenic patterns that more closely resemble the ground truth, im- proving overall image fidelity.
4KAgent (ExpSR-s4-F)
33.58
0.8678
Table 26: Quantitative comparison on Fundoscopy dataset. The top three results for cach metric are marked in bold, underline, and italic.
Dataset
Method
PSNRT SSIM
Bicubic
25.20
0.86
SRGAN
34.22
0.88
DRIVE
af. (5)
37,72
0.91
4KAgent (ExpSR-s4-E)
41.52
0.95
The fundoscopy results demonstrate 4KAgent's effectiveness in restoring details from degraded inputs. Compared to the LR image, 4KAgent's reconstruction of the retinal vascular network shows clear improvement. The method produces sharper and more continuous vessels, resolving many of the fine micro-vesse and bifurcation points that are obscured in the LR version. The resulting image more closely resembles the HR ground truth, suggesting its potential to aid in retinopathy screening from lower-resolution captures without sacrificing critical diagnostic details.
Discussions From both quantitative and qualitative perspectives, our evaluation suggests that 4Kggent capable system for cross-domain super-resolution across diverse clinical imaging modalities, showing competitive performance on X-ray, ultrasound, and fundoscopy datasets. This result is notable, as the prevailing approach often involves developing specialized models for each modality, paradigm that can be limited by poor generalization across different scanners and protocols. By not relying on domain-specific training, 4KAgents agentic framework offers a flexible alternative, adaptively deploying its tools to address the unique challenges of each image, from enhancing the clarity of lung markings in chest radiographs to defining subtle echogenic interfaces in ultrasound and resolving fine vascular networks in fundoscopy.
The ability to generate reconstructions with improved structural details may have implications for downstream applications. For example, improved sharpness in retinal vessels could aid in retinopathy screening; clearer ultrasound images help with tissue boundary delineation for segmentation; and more detailed X-rays could enhance the visibility of subtle pulmonary abnormalities. Ultimately, the
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2017 VOU---201-000
SN6RGAN
AKAppa
Xony
AKAgest (FxSSR.44))
Figure 24: Visual comparison of X- X-Ray image SR on Chest X-ray 2017 and Chest X-ray 14 dataset.
US-CASE,
4KAgent (expSR
MMUSIK, 0857
M2
4K'geet (FspSR
Figure 25: Visual comparison of Ultrasound image SR US-CASE and MMUSIK dataset.
4KAgent (ExpSR.44FF)
4KApost (ExpSR=e4-F)
Tes61
T
0
0
Test3
Test19
Figure 26: Visual comparison of fundoscopy image SR on DRIVE dataset (128-5512)
performance of our agentic approach indicates its significant potential for robust deployment across more real-world clinical workflows and imaging modalities, driven by its adaptability and inherent extensibility for incorporating more specialized medical profiles.
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G Ablation Studies and Running Time Analysis
In this section, we conduct ablation studies on core components in 4KAgent: (1) Q-MoE policy and (2) Face Restoration Pipeline. Then, we perform a running time analysis of 4KAgent.
Q-MoE policy To assess the contribution of our Q-MoE mechanism during execution and reflection, we perform an ablation study in which Q-MoE is replaced by the DFS strategy from AgenticIR [274]. denoting this variant 4KAgent (DFS). Experiments are conducted on the MiO-100 Group C dataset under the mulippe-degrrdationn image restoration setting.
As shown in Tab. 27, integrating Q-MoE leads substantial improvement' in perceptual quality. Specifically, metrics such as LPIPS, MANIQA, CLIPIQA, and MUSIQ exhibit significant gains, with minimal impact fidelity metrics like PSNR and SSIM. Furthermore, the visual comparisons presented in Fig. 27 provide additional evidence, showing that AKeget equipped with Q-MoE generates noticeably sharper and more realistic details compared to the DFS- variant.
Table 27: Ablation study Q-MoE policy. The better result is marked in bold.
Dataset
Method
PSNRT
SSIM
LPIPS
MANIQAT
CLIPIQAT
MUSIQU
4KAgent (DFS)
19.81
0.5785
0.4381
0.3286
0.4854
54.03
MI0100 Group
4KAgent (Q-MoE)
LQ
4KAgent (DFS)
4KAgent (Q-MoE)
HQ
Figure 27: Visual comparisons for ablation study Q-MoE.
Face restoration pipeline To evaluate the impact of our Face Restoration Pipeline, we conduct an ablation study on the WebPhoto-test dataset using three profiles: ExpSR-s4-P, ExpSRFR-s4-P, and GenSRFR-see P. Experimental results and the difference among these profiles are shown in Tab. 28.
Enabling the face restoration module (i.e., switching profile from ExpSR-s4-P to ExpSRFR-s4-P and GenSRFR-s4-P) yields higher face IQA scores (CLIB-FIQM and DSL-FIQA). Moreover, when setting the 'Restore Option' to 'None' rather than super-resolution we observe further improvements across both generic image IQA metrics (NIQE and MUSIQ) and face IQA metrics.
Table 28: Ablation study on face restoration pipeline on WebPhoto- Test dataset. The best and second-best results are marked in bold and underline respectively.
Method
Restore Option
Face Restore
NIQEL
CLIPIQA!
MUSIQU
MANIQAT CLIB-FIQAT DSL-FIQAT
4KAgent (ExpSRR-4-PP)
super-resolution
False
5.11
0.7119
73.62
0.6601
0.6415
0.7194
4KAgent (ExpSRER-s44 s4.P)
super-resolution
Thme
4.53
0.6600
72.89
0.6405
0,6602
0.2237
4KAgent CenSRFRRs4-PP
None
True
4.15
0,7077
75.92
0.6576
0.6671
0.7683
Visual comparisons are shown in Fig. 28. 4KAgent with GenSRFR-s4-P profile produces the finest facial details (hair details, harmony of facial area and background area). This trend indicates that WebPhoto-test images suffer from complex, mixed degradations, and 4KAgent benefits from integrating multiple restoration tasks with Q-MoE driven selection to achieve superior visual quality.
Running Time Analysis The inference time 4KAgent varies depending on the selected profile, the quality of the input image, and the length of the restoration plan. In this section, we analyze the inference time of 4Keget using NVIDIA RTX 4090 GPUs. Specifically, we report the fastest and slowest cases observed in our experiments. The fastest case involves super-resolving images
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LQ
4KAgeett (ExpSR-s4-P)
4KAgent (ExpSRFR-s44P)
4KAgent (GenSRER-s4-P)
Figure 28: Visual comparisons for ablation study on face restoration pipeline.
(x4) from the B 100 dataset using the ExpSR-s4-F profile. The slowest case corresponds to jointly restoring and upscaling low-quality images from the DIV4K-50 dataset 4K resolution under the Gen4K-P profile. The inference times for these two cases are summarized in Tab. 29,
Table 29: Inference time of 4KAgent (fastest and slowest cases on our experiments).
Profie Nickname
Task
Resolution
Benehmark
Length of Plan
Inference Time (s)
ExpSR-s4.F
Super-aesolution (4x)) x)
120 x 80 -4 480 320
B100
1.0 = 0.0
50.96 = 2.01
GenAK-P
Joint
Upscaling
DIVIK-50
As 4Kggent currently executes its tools sequentially, there is substantial potential for acceleration, for example, by running independent restoration tools in parallel each step.
H Applications and Broader Impacts
H.1 Applications
High-resolution On-Demand Media Streaming 4KAgent offers significant potential for enabling network operators, such as YouTube, Netflix, Instagram, Amazon Prime Video, TikTok, Kwai, Snap, Twitch, to name a few, to deliver 4K-quality video services from much lower-bitrate streams. For example, edge-based to store and transmit mostly lower-resolution content (e.g., IK) which can then be upscaled to 4K quality on the end-user's device using edge-based processing. This approach dramatically cuts storage and bandwidth costs (and energy use) compared to naively streaming native 4K [108]. Technologies like NVIDIA's Deep Learning Super Sampling (DLSS) [i] demonstrate the feasibility GPU chips. Integrating such real-time upscaling
can upscale IK stream to 4K at the user's device [250], allowing providers
and usability of real-time super-resolution into adaptive streaming protocols could also improve user experience by minimizing disruptive quality shifts often associated with variable network conditions, ensuring viewers consistently receive high-resolution blayback on capable displays.
Video Conferencing and Telepresence Network bandwidth constraints and limitations inherent in typical webcams smartphone cameras often necessitate transmitting video streams at resolutions lower than 4K. Implementing SR algorithms, such as 4KAgent, on the receiver's is end can effectively upscale these lower-resolution feeds. This process restores fine-grained details in facial features or gestures that might otherwise be lost, thereby enhancing the perceived visual quality and potentially aiding communication cues like lip-reading the interpretation of subtle expressions [151, 225, 112]. Consequently, even devices with modest camera capabilities can deliver an experience approximating 4K quality to the viewer, without requiring increased upload bandwidth from the sender. This democratization of high-resolution video conferencing can improve remote collaboration, making more accessible and effective for users constrained by network limitations or hardware capabilities.
Surveillance and Security Image SR technologies like 4K (with fidelity-based profile) offer significant value in enhancing footage from law enforcement operations, particularly from body-worn cameras and dashcams. These devices often capture video resolutions like 720p 1080p with
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wide fields of view, resulting in low-detail imagery, especially in challenging conditions such as low light [109]. Faces or license plates captured a distance may span only few dozen pixels, far below recommended thresholds for identification (e.g., ~006 pixels per face for courtroom evidence [2]). The quality is often further compromised by heavy compression and sensor limitations, introducing noise and motion blur. Modern SR approaches, particularly "blind" methods that model complex real-world degradations, can effectively mitigate these issues and restore detail in practical bodycam footage. By enhancing critical regions (faces, license plates) in police videos, SR can improve both human and automated identification, while preserving the veracity required for judicial use.
Similarly. public surveillance systems, including city-wide CCTV networks, border security cameras, and transit hub monitoring, face comparable challenges related resolution and image quality, Fixed cameras covering wide areas often render persons or objects of interest with very low pixel counts, with quality impacted by distance, illumination, camera motion, and aggressive compression techniques employed to manage bandwidth and storage [152]. SR provides a means to enhance detail retroactively without costly hardware upgrades. Field studies have also reported the effectiveness of SR. For example, National Institute of Justice study [3] showed that multi-resolutionn SR could reconstruct identifiable features from extremely low-resolution facial images comparable to those from real-world security cameras. Overall, SR can act as force multiplier for legacy surveillance infrastructure. enhancing situational awareness and forensic capabilities. However, the enhanced capability for identification also raises potential privacy concern,, which will be discussed in Appendix H.3.
Gaming and Entertainment SR techniques are extensively utilized in the entertainment sector to enhance visual quality while sustaining high frame rates, particularly in demanding gaming, VR, and AR applications. A prominent example is NVIDIA's DLSS, a suite of Al-powered neural rendering techniques that upscale lower-resolution frames to higher target resolutions, as high as 4K. DLSS can significantly improve performance, often more than doubling GPU throughput and leading to substantial frame rate increasesfor instance, one report indicated a boost of up to approximately 360% (e.g., from 8 to 36.8 fps on RTX 2060 at 4K). Successive iterations like DLSS 3 with Frame Generation and DLSS 3.5 with Ray Reconstruction have introduced further advancements by using Al to generate additional frames or improve ray-traced effects.
VR, AR, and XR This need for efficient, high-quality rendering extends critically to the domain of spatial intelligence and computing, as seen in advanced devices like the Apple Vision Pro, which aims to deliver experiences with more pixels than a 4K TV per eye. While such platforms boast high native display resolutions, SR techniques could play crucial role in rendering complex mixed-reality scenes or high-fidelity passthrough video efficiently, maintaining visual clarity without overwhelming the processing capabilities. Similarly, as smart glasses like the Ray- Meta Wayfarer evolve and potentially incorporate more advanced display capabilities for augmented reality overlays, SR will be key to delivering crisp digital information without excessive battery drain. Broader XR initiatives, such as Google's development of Android XR, also stand to benefit from robust SR solutions to enable a diverse ecosystem of devices to achieve compelling visual experiences. For all these platforms, from gaming consoles to sophisticated XR headsets and smart glasses, the ability of SR systems like 4KAgent adaptively enhance visual quality from various inputs will be paramount in balancing immersive, high-resolution experiences with practical performance and power constraints.
Al-Generated Content (AIGC) Production Industry Photographers, digital artists, and filmmak- ers increasingly leverage SR tools to enlarge, restore, and enhance the quality of both conventional (e.g.. old photos, archival digital footage) and even Al-generated images and video footage. We have demonstrated in Appendix E. that 4KAgent is capable of synthesizing high-fideltig details in generated media, coinciding with recent trend that generates high-resolution advertisement for KFC?, leveraging outputs from generative video models such as Google's 's Veo [191]. Luma AT's Dream Machine [135], and OpenAl's Sora [155), further enhanced using Topaz Labs Video Upscaler to achieve higher resolutions (e.g., 4K) and professional quality suitable for broader use. SR techniques are crucial for bridging this gap towards generating Aitra-high-resolutiin
content,
enabling creators to enhance these Al-generated visuals. For instance, images generated for concept art, marketing materials, or virtual environments can be significantly improved in detail and clarity through SR, making them suitable for 4K displays or fargeforrmat printing. Similarly, generative
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video content, which might be created at lower resolutions to manage computational costs, can be upscaled using specialized tools like Topaz Video AI to achieve crisper, higher-resolution results (e.g.. 4K) ready for distribution or integration into larger productions. State-of- of-the-rrt SR methods, includ- ing GGNN-aseed approaches, can synthesize photorealistic details, effectively transforming AIGC outputs into polished, professional -rrade assets. The ability of robust and adaptive SR solutions like 4KAgent to handle the diverse and sometimes unpredictable nature of AIGC makes them particularly valuable for ensuring that AI-driven creative endeavors can meet high-quality benchmarks.
Scientific Imaging High-resolution imagery is crucial across numerous scientific disciplines, par- ticularly when native sensor capabilities are constrained. In remote sensing, deep super-resolution (SR) methods significantly enhance spatial details of satellite imagery, facilitating accurate land-use classification and environmental monitoring [153, 171]. For instance, self-supervised SR techniques trained on sequences of satellite images yield sharper and less noisy results compared to raw cap- tures, substantially improving downstream geospatial analysis [153]. Microscopy and biomedical imaging similarly benefit from SR, particularly through novel quantum imaging techniques. Recent advancements by Zhang et al. and He et al. leverage quantum entanglement to achieve unprecedented imaging resolution, demonstrating quantum microscopy at the Heisenberg limit and significantly enhancing cellular and sub-cellular visualization [265, 65]. Additionally, computational SR methods applied to microscopy, like content-aware restoration for fluorescence images [215], complement these quantum techniques by computationally reconstructing detailed 3D biological structures from limited optical inputs. Thus, versatile and advanced SR frameworks such as 4KAgent, coupled with emerging quantum imaging methods, can revolutionize scientific research by providing richer, more precise imagery across multiple imaging modalities.
Medical Image Applications In medical imaging, SR facilitates detailed diagnostics by trans- forming low-dose or rapidly acquired imaging scans into high- high-fidelity medical images. Techniques employing deep learning- baaddd SR on modalities such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) have shown promise in generating accurate, high-resolution images from suboptimal inputs, thus reducing patient radiation exposure and acquisition time without sacrificing diagnostic quality [31, 269]. For instance, generative adversarial networks (GANs) and transformer- based SR approaches like TransMRI demonstrate substantial improvements in enhancing anatomical details critical for diagnostic accuracy [51]. Consequently, methods like Agentt, which provide universal super-resolution capabilities, can significantly impact clinical diagnosties by offering highly detailed and diagnostically reliable imagery from resource-efficient imaging procedures.
Embodied Al and Robotics Embodied Al systems, including robotics platforms, leverage SR to enhance visual perception, critical for tasks such as navigation, object manipulation, and human-robot interaction. Robotic visual systems frequently face limitations in sensor resolution and onboard processing capacity, challenges that SR methods can effectively address. Recent studies indicate that integrating SR into robotic vision pipelines notably improves object detection and localization, particularly for distant or small-scale objects critical in dynamic environments [141]. Furthermore, real-time lightweight SR models tailored for robotic platforms have been developed, improving perception accuracy and enabling robots to perform complex tasks efficiently, such as precise grasping and navigation through cluttered or visually challenging scenarios [7, 76]. Consequently, robust SR algorithms substantially advance robotic autonomy and operational effectiveness.
Autonomous Vehicles, Drones, and Intelligent Transportation Systems (ITS) Autonomous vehicles, acrial drones, and intelligent transportation systems increasingly depend on high-quality visual inputs for safe and efficient operation. SR technologies significantly boost the capability of onboard cameras discern critical details from lower-resolution captures under real-world constraints. For instance, SR-enhance traffic sign recognition systems, crucial for autonomous navigation and safety-critic decision- making [183, 149, 127]. Research demonstrates that training object detection models with super- resolved images significantly enhances their effectiveness challenging scenarios, including poor visibility or long-range object detection from drones or vehicle-mounted cameras [149]. Furthermore, ITS can employ SR-enhanced camera networks for robust and precise traffic monitoring and anomaly detection, improving urban mobility management and public safety.
imagery improves the detection accuracy of pedestrian, vehicle, and
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GIScience and GeoAI Geographic Information Science (GIScience) heavily utilizes spatially explicit, high-resolution data for mapping, analysis, and policy-making Deep SR has recently emerged as a powerful method for enhancing geospatial imagery resolution, substantially improv- ing the interpretability and accuracy of satellite-derived GIS datasets. For example, SR methods significantly improve the extraction accuracy of buildings, roads, vegetation, and other geographic features from medium- to low-resolution imagery, providing a cost-effective alternative to deploying expensive high-resolution satellite sensors [171, 163]. Additionally, high-quality SR outputs are vital for applications such as precision agriculture and disaster response planning, demonstrating the broad utility of universal SR frameworks like 4KAgent within GIScience research and applications.
H.2 Broader Impacts
Economic Impacts. SR technologies, exemplified by 4KAgent, drive significant economic advan- tages by enhancing operational efficiency, creating new markets, and supporting environmental sus- tainability. By reducing bandwidth, storage, and infrastructure costs associated with high high-resolution content delivery, SR solutions enable economical, high-quality media dissemination over constrained networks, benefiting digital platforms and smaller businesses [116, 251]. Companies such as BytePlus and Maxar Intelligence have successfully leveraged SR technologies to open new markets in health- care diagnostics, geospatial intelligence, and media restoration [16, 74]. Additionally, by minimizing data storage and transmission demands, SR contributes meaningfully to the environmental goals of reduced energy consumption and lower carbon emissions [101].
Accessibility. SR substantially promotes digital equity by enabling high-quality visual content access for users in regions with limited bandwidth resource constraints without necessitating advanced infrastructure or expensive devices [161]. Particularly in education and healthcare, SR supports remote learning and telemedicine by delivering clearer instructional and diagnostic imagery, significantly benefiting underserved communities [15]. Moreover, SR- SRiintegrdted assistive technolo- gies offer improved accessibility for individuals with visual impairments by enhancing image clarity and text readability, facilitating greater inclusion and interaction within the digital sphere [120, 192].
Vertical Impacts to Industry. Demand for real-time, high-quality SR capabilities stimulates technological progress across various industries, including media, robotics, autonomous systems, and scientific visualization. SR advancement drives innovation in specialized neural hardware, edge computing solutions, and embedded AI, spurring the development of powerful and efficient Neural Processing Units (NPUs) in consumer devices [18, 99]. Additionally, SR techniques significantly enhance autonomous vehicle, drone, and robotic platform capabilities by improving object detection accuracy, scene understanding, and decision- decision-making reliability, particularly in challenging operational environments [149, 171]. These impacts extend to scientific instrumentation, such as microscopy and geospatial imaging, where SR enables unprecedented detail and precision [153, 215].
H.3 Limitations and Potential Negative Societal Impacts
Efficiency and Computational Cost. SR methods, particularly deep learning based and agen- tie frameworks like 4Kgget, often require substantial computational resources for training and inference. High-resolution SR models usually rely resource-intensive GPU or TPU clusters, imposing significant energy consumption [173]. Even optimized inference can become computa- tionally burdensome the edge, potentially limiting deployment on low-powered or mobile devices unless significant model compression and acceleration techniques are applied [99, 264]. Balancing performance and efficiency remains a critical open challenge, particularly for real-time applications in resource-constrained environments.
Bias, Fairness, and Model Drift. Data-driven SR models inherit biases from their training datasets, potentially leading to uneven quality across different image categories, demographics. or scenar- ios [54, 143]. Such biases might systematically disadvantage specific groups, for instance, by inadequately resolving images related to underrepresented populations or environments. Model drift over timewhere the model gradually becomes less accurate due to changing real-world distri- butionsalso poses serious issue for practical deployment, requiring continuous monitoring and recalibration to ensure faimess and reliability [52, 133].
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Ethical Issues and Privacy. Enhanced imaging capabilities enabled by SR, particularly in surveil- lance contexts, can amplify privacy risks. The ability to recover detailed features such as faces or license plates from previously anonymized or low-resolution imagery might lead to unauthorized or unethical identification of individuals [62]. This capability necessitates clear regulatory guidelines and ethical oversight to avoid misuse [115]. Concerns are especially pronounced in contexts of law enforcement, border surveillance, and public monitoring, where SR technologies must be carefully governed to prevent potential violations of civil liberties and personal privacy [38].
Failure Modes in High-stake Settings. The adoption of SR techniques in high-stakes environ- ments, including medical diagnostics, autonomous vehicles, and security monitoring, introduces risks associated with model hallucinations or misleading image reconstructions [35]. SR models, particularly generative approaches, may produce plausible yet incorrect details absent from the original low-resolution inputs. potentially leading to erroneous interpretations or decisions [8]. In clinical settings, for example, SR-generated artifacts could result in incorrect diagnoses or overlooked medical conditions, underscoring the importance of rigorous validation and transparency regarding model uncertainty and reliability [84].
I Related Work
L11 Image Super- Resolutisn
Deep learning has significantly advanced the field of single-image Super-Resoluticn (SR). The seminal work, SRCNN [45], introduced convolutional net for SR, with primary focus on opti- mizing the Mean Squared Error between the super-resolved and high-resolution images. Following this, numerous studies have enhanced reconstruction accuracy by improving network architec- tures, including residual and dense connections [87, 121, 267], attention mechanisms [19, 41, 266), and multi-scale networks [53, 110]. While these methods perform well in modeling the poste- rior distribution of the training data, they inevitably suffer from the issue of overly smooth visual results [98, 147, 226]. In recent years, significant efforts have been made to develop generative model-based SISR techniques that produce more visually appealing results. These include autoregres- sive models [40, 146, 190], GAN-based models [98, 201. 256, 21, 118, 105], and diffusion- based models [195, 234, 123, 217, 168, 205, 216]. SRGAN [98], as a pioneering GAN-based SR model, assumes image degradation through bicubic downsampling and generates photo-realistic images. BSRGAN [256] and Real-ESRGAN [201] achieve promising real-world SR results by using ran- domly shuffled degradation and higher-order degradation. SwinlR [119] replaces the CNN-based generator network with visual transformers, leading to more stable training and more realistic textures. Additionally, SeD [105] introduces a semantic-aware discriminator to capture fine-grained distribu- tions by incorporating image semantics as a condition. Recent diffusion-based models have focused on fine-tuning the Stable Diffusion model for reconstructing high-quality images, using low-quality images as control signals. Notably, StableSR [195] fine-tunes time-aware encoder and employs feature warping to balance fidelity and perceptual quality. SceSR [217] introduces degradation-robus tag-style text prompts to enhance the semantic awareness of the Real-ISB model. Furthermore, recent studies on diffusion-based models, such as SinSR [205], OSEDiff [216), PiSA-SR [179], and GuideSR [9] achieve one-step image super-resolution.
1.2 Image Restoration
Recent advances in deep learning have led remarkable progress in blind image restoration tasks, including denoising, deblurring, deraining, dehazing, and removal of JPEG compression artifacts. Early works such as ARCNN [44] demonstrated the potential of compact convolutional neural networks, particularly in the context of image denoising. Since then, broad range of sophisticated network architectures and training strategies have been developed to further enhance restoration performance. These include the use of residual blocks [255, 131, 257], attention mechanisms [242, 25, 186, 57, 247], and Transformer- based designs [189, 275, 246, 206]; as well as generative paradigms such as GANs [58, 26, 11, 56. 73, 147, 158, 128] and diffusion models [123, 221, 81, 204, 82, 50]. Notably, general-purpose restoration models like Uformer [206), MAXIM [186), Restormer [246], and NAFNet [25] have demonstrated strong performance across diverse restoration tasks, often trained independently for each specific degradation type. However, such single-degradation methods often struggle in weal-world scenarios where multiple types of degradations co-exist. This limitation
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has sparked growing interest in the emerging field of All-in-One image restoration, which aims to build unified models capable of handling wide range of degradations with single network [107. 138, 159, 236, 252, 189]. For instance, AirNet [107] introduces a degradation classifier trained via contrastive learning to guide restoration, while ADMS [159] employs a multi-type degradation classifier to dynamically select Adaptive Discriminant filters, enabling degradation-specife parameter modulation within the restoration network.
1.3 LLM Agents
Advancements in LLM-based frameworks have enabled more structured reasoning and agent de- signs. particularly for complex multimodal tasks. Initial efforts emphasized improving reasoning capabilities through refined prompting strategies and modular architecture. Chain-of- Thouuh (CoT) prompting [213] introduced stepwise reasoning, facilitating decomposition and interpretability across diverse tasks. ReAct [237] combined reasoning with tool interaction by interleaving thought traces and external actions, supporting more adaptive behavior. Extending this direction, CoALA [177] formalized components such as memory, reasoning, and control within cognitive architecture, offering a modular design space for building general-purpose language agents. These developments established basis for on these foundations, application-drive LLM agents have been developed to incorporate tool use and dynamic peccision-makingg within specialized domains. In vision tasks, MMCTAgent [94], VideoA- gent [197]. and ReAgent-Y [273] implement planning and evaluation pipelines for image and video analysis, incorporating external modules for retrieval and verification. In the medical domain, agents such as MedCoT [130], CLINICR [150), and MMed/ggentRRL [224] employ hierarchical reasoning frameworks address clinical questions, integrating structured logic and domain-specific knowledge to enhance interpretability and decision quality.
agent systems with integrated reasoning pipelines. Building
Similarly, LLM-based agents have also emerged as promising paradigm for tackling complex image restoration tasks involving multiple degradations. RestoreAgent [22] pioneered the use of MILMS for autonomous task identification, model selection, and execution planning. AgenticIR [274] introduced a sive-stage numan-inspiree workflowPerception,, Scheduling, Execution, Reflection, and with self-exploration to build IR-specific experience. MAIR [80] advanced this by employing a multi-agent system guided by real-world degradation priors, improving both efficiency and scalability. HybridAgent [104] proposed hybrid interaction scheme with fast and slow agents, along with a mixed-distortion removal strategy to mitigate error propagation. Q-Agent [272] further introduces quality-driven chain-of-thought framework, leveraging no-reference IQA metrics to guide greedy restoration without costly rollbacks. These works demonstrate the growing potential of combining general-pe pprppss language intelligence with visual tools for robust, adaptive image restoration. More recently, JarvisIR [124] and JarvisArt [125] leveraged intelligent agent workflows perform task-oriented image restoration and creative photo retouching.
Reschedulingauggenntt
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Reviews:
[{"review_id": "uPDEozXz54", "reviewer": "Reviewer_APnX", "summary": "A first agentic framework was proposed that unifies classical, real-world, and multi-degradation super-resolution under a single, highly customizable system. By leveraging a two-stage Perception Agent–Restoration Agent architecture, enriched with the proposed Quality-Driven Mixture-of-Experts policy and a dedicated face-restoration pipeline, 4KAgent achieves state-of-the-art performance across a spectrum of benchmarks. Beyond standard benchmarks, the versatility of 4KAgent was demonstrated on AIGC imagery, remote sensing, fluorescence microscopy, and biomedical scans, highlighting its broad applicability.", "questions": "In line 55, the paper claims that 4KAgent can achieves the arbitrary-scale super-resolution, but in the implement line 112 and line 180, only specific integer upscaling factors were provided. It needs to be clarified.\nWhy conduct twice super-resolution in perception agent and restoration agent Figure 2? Please provide the detailed explanation. \nSome writing errors should be corrected, such as line 54 left parenthesis. Please review the entire paper.", "limitations": "Please see the weaknesses and questions.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths \nSufficient novelty and experiments.\nA first agentic framework was proposed that unifies classical, real-world, and multi-degradation super-resolution under a single, highly customizable system. By leveraging a two-stage Perception Agent–Restoration Agent architecture, enriched with the proposed Quality-Driven Mixture-of-Experts policy and a dedicated face-restoration pipeline, 4KAgent achieves state-of-the-art performance across a spectrum of benchmarks. Significantly enhancing facial details in portrait and selfie photos.We rigorously evaluate our 4KAgent across 12 distinct task categories encompassing a total of 26 diverse benchmarks, setting new state-of-the-art on a broad spectrum of imaging 15 domains.\n \nWeaknesses", "was_revised": true, "final_justification": "My concerns were addressed. I keep my score.", "rebuttal": "We sincerely thank Reviewer APnX for recognizing the novelty of our proposed agentic framework, the comprehensiveness of our experimental validation, and the broad applicability of our method. We greatly appreciate your positive feedback on the unified design of 4KAgent, the effectiveness of our two-stage Perception-Restoration Agent architecture, the introduction of the Quality-Driven Mixture-of-Experts policy, and the dedicated face-restoration pipeline. Your acknowledgment of our state-of-the-art performance across various benchmarks, as well as the impact of our work on diverse domains such as AIGC, remote sensing, and biomedical imaging, is very encouraging.\n\n>**Q1: The misalignment between line 112 / 180 and line 55**\n\n**A1**: Thank you for pointing this out. As we have shown in line 112 and line 180 in the main paper, 4KAgent is able to upscale an image with a scale factor of 2, 4, 8, or 16. It can achieve a larger scale factor (e.g., 32, 64, …) if applied recursively. However, it still has misalignment with ‘arbitrary-scale super-resolution’ in line 55. Therefore, we will provide a more rigorous statement in line 55 in the revised version of the paper.\n\n>**Q2: Conduct twice super-resolution in perception agent and restoration agent in Figure 2**\n\n**A2**: In a single run, 4KAgent can upscale images with a resolution of 250 x 250 or larger to 4K resolution (the long side of the image is greater than 4000 pixels). This depends on the profile set by the user. Specifically, after perceiving the distortion of the input image and the corresponding restoration agenda, the Perception Agent will calculate the scale factor based on the resolution of the input image and the 4K resolution. The calculation method is given by the formula in line 112. The resolution of the input image in Figure 2 is 259 x 194. Therefore, the scale factor required to enlarge this image to 4K resolution is 16. To our best knowledge, SOTA super-resolution (SR) methods (e.g., SwinIR [1], HAT-L [2], DiffBIR [3], OSEDiff [4], PiSA-SR [5], …) can not directly achieve 16x SR or perform limited. We also conduct an experiment on DIV4K-50 benchmark to explore the performance of applying super-resolution (x4) twice and directly applying a 16x super-resolution. The experiment result is shown below:\n\n| Method           |  NIQE &#8595;   |   CLIPIQA &#8593;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|\n| OSEDiff [4] (4x &#8594; 4x)  |   4.88 |    0.7201    |   39.88   |  0.5482  |\n| OSEDiff [4] (16x)  |   8.37  |    0.5680    |   25.07   |  0.4210  |\n| PiSA-SR [5] (4x &#8594; 4x)  |   5.01  |    0.7141    |   38.22   |  0.5364  |\n| PiSA-SR [5] (16x)  |   9.30  |    0.5549    |   24.51   |  0.3861  |\n| **4KAgent** (Direct 16x SR)  |    8.91     |    0.4780          |     22.78      |      0.3939    |\n| **4KAgent** |   **3.15**  |    **0.7585**    |   **44.16**   |  **0.5928**  |\n\nIt shows that applying super-resolution (x4) twice performs better than directly applying 16x super-resolution for SOTA SR methods and our 4KAgent. Therefore, we add two super-resolution (x4) tasks in the restoration agenda to achieve the goal of 16x upscaling, and then schedule the restoration plan based on the updated restoration agenda.\n\n>**Q3: Correct writing errors**\n\n**A3**: Thank you for pointing this out. We will carefully check the writing errors in the paper and correct them in the next version.\n\n**Reference**\n\n[1] Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., & Timofte, R. (2021). Swinir: Image restoration using swin transformer. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 1833-1844).\n\n[2] Chen, X., Wang, X., Zhang, W., Kong, X., Qiao, Y., Zhou, J., & Dong, C. (2023). Hat: Hybrid attention transformer for image restoration. arXiv preprint arXiv:2309.05239.\n\n[3] Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., ... & Dong, C. (2024, September). Diffbir: Toward blind image restoration with generative diffusion prior. In European conference on computer vision (pp. 430-448). Cham: Springer Nature Switzerland.\n\n[4] Wu, R., Sun, L., Ma, Z., & Zhang, L. (2024). One-step effective diffusion network for real-world image super-resolution. Advances in Neural Information Processing Systems, 37, 92529-92553.\n\n[5] Sun, L., Wu, R., Ma, Z., Liu, S., Yi, Q., & Zhang, L. (2025). Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach. In Proceedings of the Computer Vision and Pattern Recognition Conference (pp. 2333-2343)."}, {"review_id": "V2JTRj9tol", "reviewer": "Reviewer_wkHw", "summary": "In this paper, the authors proposed a image enhancement system which consist of a profiling module, a perception agent and a restoration agent, the authors argue the system could universally upscale any image to 4K resolution.", "questions": "1) Recently, generative SR methods have obtained promising results for super-resolve low-resolution images with complex degradations, I am not sure if image super-resolution need to introduce such a complex system. Although some generative SR approaches have been adopted as comparison methods, the comparison is not fair because the authors establish new experimental settings, I suggest the authors to follow the experimental settings of recent SOTA generative SR methods, and conduct a side-by-side comparison with them. Then we can see the advantage of introducing such a complex system.\n2) Can the authors discuss more about how natural language descriptions could benefit SR model for recovering low-level details?\n3) Following the above question 2), I think the authors need to provide ablation studies to analyze the advantage of their design choice. For example, the good results come from good restoration plan or powerful tools? I think the authors should give examples of how different tools working together to deal with specifc images, for real-world image supre-resolution task, I think most of tools will not be used.\n4) Can the authors report some information about runtime?\n5) I do not think rain-streak removal should be automatically conducted in an image resotraiton system, because people do take pictures in raining days.", "limitations": "1) I think this paper introduces a system with unnecessary complex modules to deal with a fake problem. Many components in the \"toolbox\" is unnecessary for the SR task, for example, people usually do not change image brigtness and conduct dehazing/deraining when they super-resolve images. Instead of fucusing on irrelevant tasks like deraining/dehazing (irrelevant to SR), I think the authors should pay more attention to noise and artifacts, which is the major difficulties in SR. The authors could mimic the processing ISP and generate low quality image with special artifacts,  for example, processing the challenging see in the dark dataset with a conventional denoiser which will leave some method noise, and aply gamma correction, color correction and tone mapping, then you will get a image with spatial variant structural artifacts, this kind of images are real \"real-world low-quality images\" and I am curious if the propose method could deal with such spatial vairant structural artifacts? If you just generate fake low-quality images with cascade known-degradations (perfect Gaussian noise, defocus blur, etc.), it is not a significant thing that you can deal with the image by trying different combination of cascade processing pipelines. \n2) The authors established a lot of new experimental settings which has not been considered by previous methods, while interesting new settings helps reads understand the novelty of this paper, I think beating the existing methods under standard settings (adopted by previous methods) is also very important, otherwise, I am not able to evaluate the necessarity of introducing such complex system.", "rating": 2, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "1) The authors have shown some interesting results in the paper.\n2) The paper is well-writen.\n3) The authors have evaluated the proposed method on many datasets.", "was_revised": true, "final_justification": "As I have listed clearly in my comments, I think this paper just introduces a system with unnecessary complex modules to deal with a fake problem. Many design choice have not been validated in the paper, and the authors just establish new experimental settings to show the \"advantage\" of the proposed system.", "rebuttal": "We sincerely thank Reviewer wkHw for your positive feedback. We appreciate your recognition of the overall clarity of our writing, the interesting results presented, and the thorough evaluation of our proposed image enhancement system across a wide range of datasets. Your comments are encouraging and affirm the value of our work.\n\n> **Q1 & L2: Follow the experimental settings of recent SOTA generative SR methods, and conduct a side-by-side comparison**\n\n**A1**: We thank the reviewer for pointing it out. In Section 3.2 in the paper, we adopt the same experimental setup as recent state‐of‐the‐art generative super-resolution (SR) models (e.g., SinSR [1], OSEDiff [2], PiSA‑SR [3]) and perform 4x SR on real-world image SR benchmarks: the RealSR and DRealSR datasets. Quantitatively, 4KAgent outperforms SOTA generative SR methods on perceptual metrics (NIQE, CLIPIQA, MUSIQ, and CLIPIQA). We also provide experimental results on the DRealSR dataset here:\n\n| Method                          |   PSNR↑  |  SSIM↑   | LPIPS↓  | DISTS↓  |  FID↓   | NIQE↓  | CLIPIQA↑ | MUSIQ↑ | MANIQA↑ |\n|:-------------------------------|:---------:|:--------:|:-------:|:-------:|:-------:|:------:|:---------:|:------:|:-------:|\n| DiffBIR [4]                 | 26.84     | 0.6660   | 0.4446  | 0.2706  | 167.38  | 6.02   | 0.6292    | 60.68  | 0.5902  |\n| SinSR [1]                 | **28.41**     | 0.7495   | 0.3741  | 0.2488  | 177.05  | 7.02   | 0.6367    | 55.34  | 0.4898  |\n| OSEDiff [2]                 | 27.92     | **0.7835**   | 0.2968  | **0.2165**  | 135.29  | 6.49   | 0.6963| 64.65  | 0.5899  |\n| PiSA-SR [3]                 | 28.31     | 0.7804|**0.2960**|0.2169|**130.61** | 6.20   | 0.6970    | 66.11  | 0.6156|\n| AgenticIR [5]                | 23.06     | 0.6145  | 0.4775  | 0.2973  | 182.02  | 6.11   | 0.6542    | 63.59  | 0.5927  |\n| **4KAgent**       | 23.11     | 0.6126   | 0.4579  | 0.2866  | 178.36  | **4.65** | **0.7092** | **69.30**| **0.6219**  |\n\n\nQualitative results, which we provided in section C.2 in the Appendix, further illustrate that 4KAgent produces images with sharper and more realistic details. This demonstrates that, despite the strong performance of existing generative SR approaches on real‐world images, 4KAgent surpasses them by decoupling the complex distortion in the image, performing the corresponding restoration task while using the Q-MoE strategy to select the image's highest quality, which yields superior perceptual quality.\n\nFor the 16x SR experiments in Section 3.4 and the 4K upscaling experiments in Section 3.5, 4KAgent operates in a training‑free manner: we apply it directly to the 16x SR task without additional model training. To ensure a fair comparison, we likewise treat the leading generative SR models as training‑free in this setting and evaluate them in both the 4x->4x and the 16x strategy. We presented visual comparison in the main paper,  showing that 4KAgent produces more natural and realistic details. Quantitative results in sections C.5 and C.6 in the Appendix confirm that 4KAgent achieves the state of the art performance on NIQE, MUSIQ, and CLIPIQA metrics.\n\n> **Q2: How natural language descriptions could benefit SR model for recovering low-level details?**\n\n**A2**: Natural language description is used in the reflection stage to select the image with the best quality (based on the alignment to the image description). It is not used in single SR model in 4KAgent system.\n\n> **Q3: Provide ablation studies to see good results comes from good restoration plan or powerful tools, and analysis tool usage on real-world image super-resolution task**\n\n**A3**: Thank you for pointing this out. We conduct an ablation study on the DIV4K-50 dataset for 16x super-resolution. Specifically, we fix the plan as 'super-resolution &#8594; super-resolution' and apply different SOTA generative SR methods (DiffBIR [4], OSEDiff [2], PiSA-SR [3]) as the tool in each super-resolution step. We use 3 metrics for evaluation: NIQE, MUSIQ, and MANIQA-pipal. Experiment results are shown below:\n\n| Method           |  NIQE &#8595;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |\n|----------------|:---------:|:--------------:|:-----------:|\n| 4KAgent (super-resolution@DiffBIR &#8594; super-resolution@DiffBIR)  |   3.36  |    37.17   |   0.5916   |\n| 4KAgent (super-resolution@OSEDiff &#8594; super-resolution@OSEDiff)  |   4.88  |    39.88    |  0.5482   |\n| 4KAgent (super-resolution@PiSA-SR &#8594; super-resolution@PiSA-SR) |   5.01  |    38.22    |   0.5364   |\n| **4KAgent** |   **3.15**  |   **44.16**    |   **0.5928**   |\n\nIt shows that using powerful tools can achieve good perceptual quality, but combining them with a good plan can further improve perceptual quality.\n\n> **Q4: Report some information about runtime**\n\n**A4**: Thank you for pointing this out. As a multi-agent system, 4KAgent supports multi-GPU deployment. Specifically, 4KAgent assigns different agents (Perception Agent, Restoration Agent) to different GPUs to conserve memory. Most of our experiments were conducted using two NVIDIA RTX 4090 GPUs. Therefore, we evaluate the running time using NVIDIA RTX 4090 GPUs. The running time of 4KAgent depends on the chosen profile, the quality of the input image, and the length of the restoration plan. We report the fastest and slowest cases observed in our experiments. The fastest case is super-resolve images (x4) on the B100 dataset under ExpSR-s4-F profile. The slowest case is jointly restoring and upscaling LQ images on the DIV4K dataset to 4K resolution under Gen4K-P profile. The running time under these two cases is shown below:\n\n| Profile Nickname           |  Task  |   Resolution   |   Benchmark   |  Length of plan  |  Running time (s)  |\n|----------------|---------|--------------|:-----------:|:----------:|:-----------------:|\n| ExpSR-s4-F |   super-resolution (x4)      |     120 x 80 &#8594;  480 x 320        |     B100      |    1.0 &plusmn; 0.0      |   50.96 &plusmn; 2.01        |\n| Gen4K-P |   joint restoration + 4K Upscaling  |   256 x 256 &#8594;  4096 x 4096     |   DIV4K-50  |  3.4 &plusmn; 0.6  |    1551.76 &plusmn; 230.73      |\n\nFor accelerating 4KAgent, we developed a Fast4K mode for 4KAgent. Specifically, when the image resolution is higher than the resolution threshold s set by the user (longer side > s), 4KAgent will disable long-running methods in the toolbox in order to reduce the processing time of 4KAgent. At the same time, this resolution threshold s can be set by the user, so the user can flexibly adjust it to balance performance and processing time.\n\nBased on the Fast4K mode, we conduct an experiment on DIV4K-50 by setting different resolution threshold s (s = 256 / 1024 / 4096) in 4KAgent and evaluate the running time and performance. Experiment result is shown below:\n\n| Method           |  NIQE &#8595;   |   CLIPIQA &#8593;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |  Avg. Running Time (s)  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|:-----------------:|\n| DiffBIR [4] (16x)  |   **2.65**  |    0.7078    |   38.59   |  0.5858  |       976.2          |\n| OSEDiff [2] (16x)  |   8.37  |    0.5680    |   25.07   |  0.4210  |       106.6          |\n| PiSA-SR [3] (16x)  |   9.30  |    0.5549    |   24.51   |  0.3861  |       107.8          |\n| **4KAgent** (s=1024) |    3.51     |    0.7310          |     **44.51**      |     0.5689     |        582.7         |\n| **4KAgent** (s=4096) |   3.15  |    **0.7585**    |   44.16   |  **0.5928**  |     1551.8      |\n\nIt shows that, simply set s to 1024, the average running time of 4KAgent decreases from 1551.8s to 582.7s (62.3% &#8595;) with minimal effect on performance. Under this setting, 4KAgent is even faster than the model-based approach (DiffBIR).\n\n> **Q5: Rain-streak removal should not automatically conducted in an image restoration system**\n\n**A5**: Thank you for pointing it out. One of the core contributions of 4KAgent is that it is a highly customizable system. By setting different profiles, 4KAgent can be customized to perform different image restoration tasks. We provide specific profile settings and examples in Section A.1 in the Appendix. Therefore, for images containing rain streaks, users can flexibly choose whether to perform rain removal.\n\n> **L1: Handle real-world data**\n\n**A6**: Thank you for pointing this out. In section 3.4, we test 4KAgent on RealSRSet, where each low-quality image is collected from the real world. Therefore, it contains real-world noise and artifacts. We upscale these images by a scale factor of 16 and evaluate the result images by 3 metrics: NIQE, MUSIQ, and MANIQA-pipal. Experiment results are shown below:\n\n| Method           |  NIQE &#8595;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |\n|----------------|:---------:|:--------------:|:-----------:|\n| DiffBIR [4] (4x &#8594; 4x)  |   3.63  |  44.86   |  0.6076  |\n| OSEDiff [2] (4x &#8594; 4x)  |   5.40  |   48.42   |  0.5362  |\n| PiSA-SR [3] (4x &#8594; 4x)  |   5.70  |  48.20   |  0.5464  |\n| **4KAgent** |   3.53  |  50.84  |  0.5913 |\n\nIt shows that 4KAgent performs on par or better than SOTA generative SR methods, indicating its ability to handle real-world data.\n\n**Reference**\n\n[1] Wang, Y., Yang, W., Chen, X., Wang, Y., Guo, L., Chau, L. P., ... & Wen, B. (2024). Sinsr: diffusion-based image super-resolution in a single step. CVPR.\n\n[2] Wu, R., Sun, L., Ma, Z., & Zhang, L. (2024). One-step effective diffusion network for real-world image super-resolution. NeurIPS.\n\n[3] Sun, L., Wu, R., Ma, Z., Liu, S., Yi, Q., & Zhang, L. (2025). Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach. CVPR.\n\n[4] Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., ... & Dong, C. (2024). Diffbir: Toward blind image restoration with generative diffusion prior. ECCV.\n\n[5] Zhu, K., Gu, J., You, Z., Qiao, Y., & Dong, C. (2024). An intelligent agentic system for complex image restoration problems. arXiv preprint arXiv:2410.17809."}, {"review_id": "t71MHtprdG", "reviewer": "Reviewer_jm8i", "summary": "4KAgent presents a unified, agentic framework for up-scaling any image—whether real photos, AI‑generated art, scientific scans, or medical imagery—to 4K resolution. It employs a two‑stage pipeline—first a Perception Agent that dynamically plans restoration steps via LLM/VLM reasoning, then a Restoration Agent that executes those steps using a mixture‑of‑experts toolbox with rollback capability—and demonstrates state‑of‑the‑art results across 26 diverse benchmarks.", "questions": "(1) Could you provide quantitative runtime and memory measurements (e.g., end‑to‑end latency on a standard GPU) for typical restoration tasks, and compare them against specialist SR models? This would clarify practical deployment trade‑offs.\n\n(2) When restored faces are pasted back into the original image, do visible seams or boundary artifacts occur? \n\n(3) Could the authors expand the related work section to include more recent and representative super-resolution methods, particularly generalist or unified frameworks?", "limitations": "YES", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**Strengths:**\n\n(1) 4KAgent is the first single model capable of upscaling any image—real photographs, AI‑generated content, scientific scans—to 4K resolution via a unified pipeline, avoiding the need for specialist networks per domain.\n\n(2) The two‑stage Perception Agent+Restoration Agent architecture, augmented with a Quality‑Driven Mixture‑of‑Experts policy and rollback mechanism, enables dynamic, content‑aware restoration plans and optimization at each step.\n\n(3) The authors evaluate on 26 benchmarks across classical SR, RealSR, multi‑degradation restoration, face enhancement, large‑factor upscaling, AIGC, remote sensing, microscopy, and biomedical imaging—demonstrating consistent SoTA performance and real‑world applicability.\n\n\n**Weaknesses:**\n\n(1) The multi‑agent framework, large toolbox of restoration tools, LLM/VLM components, and rollback loops introduce significant engineering and inference overhead, potentially hindering real‑time or resource‑constrained deployment.\n\n(2) The pipeline involves restoring faces separately and pasting them back into the original image, but the paper does not analyze whether this operation introduces boundary artifacts or inconsistencies, especially under misalignment or inconsistent lighting conditions.\n\n(3) The related work section provides limited discussion of existing super-resolution methods, especially recent advances in generalist or large-scale SR models.", "was_revised": true, "final_justification": "I read the other reviews and the authors’ rebuttal. My concerns have been well addressed by the authors. I suggest borderline acceptance of the paper.", "rebuttal": "We sincerely thank Reviewer jm8i for your detailed and encouraging feedback. We appreciate your recognition of 4KAgent as a unified, domain-general framework for 4K image restoration across real photos, AI-generated content, and scientific/medical imagery. We are grateful for your acknowledgement of our two-stage Perception–Restoration Agent architecture, and the Quality-Driven Mixture-of-Experts policy with rollback. Your recognition of our extensive evaluation across various benchmarks and the consistent state-of-the-art performance across diverse and practical scenarios is especially motivating for us.\n\n> **Q1: Runtime and memory measurements**\n\n**A1**: Thank you for pointing this out. As a multi-agent system, 4KAgent supports multi-GPU deployment. Specifically, 4KAgent assigns different agents (Perception Agent, Restoration Agent) to different GPUs to conserve memory. Most of our experiments were conducted using two NVIDIA RTX 4090 GPUs. For accelerating 4KAgent, we developed a Fast4K mode for 4KAgent.\nSpecifically, when the image resolution is higher than the resolution threshold s set by the user (longer side > s), 4KAgent will disable long-running methods in the toolbox in order to reduce the processing time of 4KAgent. At the same time, this resolution threshold s can be set by the user, so the user can flexibly adjust it to balance performance and processing time.\n\nBased on the Fast4K mode, we conduct an experiment on DIV4K-50 by setting different resolution threshold s (s = 1024 / 4096) in 4KAgent and evaluate the running time and performance. Experiment result is shown below:\n\n| Method           |  NIQE &#8595;   |   CLIPIQA &#8593;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |  Avg. Running Time (s)  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|:-----------------:|\n| DiffBIR [1] (16x)  |   **2.65**  |    0.7078    |   38.59   |  0.5858  |       976.2          |\n| OSEDiff [2] (16x)  |   8.37  |    0.5680    |   25.07   |  0.4210  |       106.6          |\n| PiSA-SR [3] (16x)  |   9.30  |    0.5549    |   24.51   |  0.3861  |       107.8          |\n| **4KAgent** (s=1024) |    3.51     |    0.7310          |     **44.51**      |     0.5689     |        582.7         |\n| **4KAgent** (s=4096) |   3.15  |    **0.7585**    |   44.16   |  **0.5928**  |     1551.8      |\n\nIt shows that, simply set s to 1024, the average running time of 4KAgent decreases from 1551.8s to 582.7s (62.3% &#8595;) with minimal effect on performance. Under this setting, 4KAgent is even faster than the model-based approach (DiffBIR). We will keep working on accelerating 4KAgent to increase its availability.\n\n> **Q2: Boundary artifacts for face restoration pipeline**\n\n**A2**: **TL;DR: The face restoration pipeline will introduce boundary artifacts in a low frequency of occurrence, and the benefits of the face restoration pipeline in 4KAgent outweigh the impact of the boundary artifacts it introduces.**\n\nThank you for pointing this out. When designing the face restoration pipeline in 4KAgent, we follow the ‘whole image restoration’ procedure from previous methods (e.g., GFPGAN [4], CodeFormer [5], DifFace [6]). Specifically, we use the toolbox in the GFPGAN method to extract faces in the image and paste them back after restoration. Therefore, it may introduce boundary artifacts at the edge. We conduct an experiment for this.\n\nSpecifically, we use WebPhoto-Test as the testing dataset and select 100 LQ images as test images. Then, we input these images to GFPGAN, CodeFormer and DifFace for face restoration under the ‘whole image restoration’ procedure. We also collect result images from 4KAgent. After obtaining all the result images, we conduct a user study to explore the boundary artifacts. We define the boundary artifacts as: **Jagged edges or color halos along the face boundary**. Based on this definition, we ask users to evaluate the result images and count the occurrence of the boundary artifacts. Then we calculate the average occurrence of the boundary artifacts. Experiment results are shown below:\n\n| Method           |  GFPGAN   |   CodeFormer   |   DifFace  |  4KAgent |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|\n| Avg. boundary artifacts  |   9.75 / 100  |   8.75  / 100    |   25.75  / 100   |  7.75 / 100  |\n\nIt shows that SOTA face restoration methods and 4KAgent introduce boundary artifacts following the ‘whole image restoration’ procedure. However, the frequency of occurrence is low (less than 10%), and 4KAgent does not increase the frequency of occurrence due to its system design.\n\nWe also conduct an ablation study of the face restoration pipeline in Section D in the Appendix. Specifically, we compare the result images from 4KAgent with different profiles (ExpSR-s4-P, ExpSRFR-s4-P) on the WebPhoto-Test dataset. The key difference between profiles and corresponding experiment results is shown below:\n\n| Method           |  Restore Option  |   Face Restore   |   NIQE &#8595;   |  CLIB-IQA &#8593;  |  DSL-FIQA &#8593;  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|:-----------------:|\n| 4KAgent (ExpSR-s4-P) |   super-resolution      |     False         |     5.11      |    0.6415      |       0.7194          |\n| 4KAgent (ExpSRFR-s4-P) |   super-resolution  |   True    |   4.53  |  0.6602  |     0.7237      |\n\nwhere ‘Face Restore’ controls the activation of the proposed face restoration pipeline. It shows that when activating the face restoration pipeline, both the overall image perceptual quality and face-related IQA metrics are enhanced. We also provide a visual comparison in Figure 8 in the Appendix, which further demonstrates the superiority of the face restoration pipeline by the enhanced facial and hair details from the image from 4KAgent with the ExpSRFR-s4-P and GenSRFR-s4-P profiles (activating the face restoration pipeline).\n\nTherefore, following the previous procedure, the face restoration pipeline in 4KAgent will introduce boundary artifacts with a low frequency of occurrence. However, the face restoration pipeline will enhance the overall image quality and face quality. We believe that the benefits of the face restoration pipeline in 4KAgent outweigh the impact of the boundary artifacts it introduces. We will further modify the face restoration pipeline to reduce the boundary artifacts it introduces.\n\n> **Q3: Expand the related work section**\n\n**A3**: Thank you for pointing this out. Due to space limitations, the related work section we provide in the main paper is an abbreviated version. We discuss the latest progress of SR models in detail in section B.1 of the Appendix. For example, we discussed the recent progress of generative SR methods (e.g., StableSR [7], SeeSR [8], OSEDiff [2], PiSA-SR [3]) and all-in-one image restoration methods (e.g., AirNet [9], ADMS [10]). We also discuss the recent progress of the agentic image restoration framework (e.g., RestoreAgent [11], AgenticIR [12], MAIR [13], Q-Agent [14])\n\nAt the same time, we also discuss the progress and application of SR models in Section G of the Appendix. For example, we discuss the application of SR models in video conferencing, surveillance, and gaming. We will continue to conduct in-depth research on the progress of SR models and provide a richer and more comprehensive discussion on the related work section in the next version of the paper.\n\n**Reference**\n\n[1] Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., ... & Dong, C. (2024). Diffbir: Toward blind image restoration with generative diffusion prior. ECCV.\n\n[2] Wu, R., Sun, L., Ma, Z., & Zhang, L. (2024). One-step effective diffusion network for real-world image super-resolution. NeurIPS.\n\n[3] Sun, L., Wu, R., Ma, Z., Liu, S., Yi, Q., & Zhang, L. (2025). Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach. CVPR.\n\n[4] Wang, X., Li, Y., Zhang, H., & Shan, Y. (2021). Towards real-world blind face restoration with generative facial prior. CVPR.\n\n[5] Zhou, S., Chan, K., Li, C., & Loy, C. C. (2022). Towards robust blind face restoration with codebook lookup transformer. NeurIPS.\n\n[6] Yue, Z., & Loy, C. C. (2024). Difface: Blind face restoration with diffused error contraction. TPAMI.\n\n[7] Wang, J., Yue, Z., Zhou, S., Chan, K. C., & Loy, C. C. (2024). Exploiting diffusion prior for real-world image super-resolution. IJCV, 132(12), 5929-5949.\n\n[8] Wu, R., Yang, T., Sun, L., Zhang, Z., Li, S., & Zhang, L. (2024). Seesr: Towards semantics-aware real-world image super-resolution. CVPR.\n\n[9] Li, B., Liu, X., Hu, P., Wu, Z., Lv, J., & Peng, X. (2022). All-in-one image restoration for unknown corruption. CVPR.\n\n[10] Park, D., Lee, B. H., & Chun, S. Y. (2023). All-in-one image restoration for unknown degradations using adaptive discriminative filters for specific degradations. CVPR.\n\n[11] Chen, H., Li, W., Gu, J., Ren, J., Chen, S., Ye, T., ... & Zhu, L. (2024). Restoreagent: Autonomous image restoration agent via multimodal large language models. NeurIPS.\n\n[12] Zhu, K., Gu, J., You, Z., Qiao, Y., & Dong, C. (2024). An intelligent agentic system for complex image restoration problems. arXiv preprint arXiv:2410.17809.\n\n[13] Jiang, X., Li, G., Chen, B., & Zhang, J. (2025). Multi-Agent Image Restoration. arXiv preprint arXiv:2503.09403.\n\n[14] Zhou, Y., Cao, J., Zhang, Z., Wen, F., Jiang, Y., Jia, J., ... & Zhai, G. (2025). Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model. arXiv preprint arXiv:2504.07148."}, {"review_id": "MqsAeRsRlQ", "reviewer": "Reviewer_eAhT", "summary": "This work proposes an agentic super-resolution system to upscale any image to 4k resolution. The framework contains perception agent to provide restoration plan with deployed VLM and restoration agent to execute step by step with various restoration models stand by. The face restoration is particularly optimized with embedded detection and restoration sub-system. DIV4K-50 is built as challenging benchmark for 4K SR. Extensive experiments are conducted across various domains, including satellite image, scientific images, AI-Generated image, and so on, validating the effectiveness of the method.", "questions": "1. I think the proposed pipeline could be a potential framework to investigate the traditional restoration models through observing the winning rate in the MoE policy in face with different images, which may identify the existing restoration models that suited for different domains.", "limitations": "The authors have provided the limitations of their work.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "**Strength:**\n1. The authors build a systematic agent framework for 4KSR and validate its effectiveness on various domains;\n2. The overall pipeline is clear with embedded plan-execution-evaluation-reflection, and the face restoration is particularly optimized for practical application;\n3. The authors expose some configurations to enable user to build its own customized 4KSR agent;\n\n**Weaknesses:**\n1. It is better to provide the fast/slow option to balance the performance and process time, as current execution step may be time-consuming to execute all models in the toolbox for evaluation, which could be further optimized;\n2. It is wondered whether paste the restored face back could bring unnatural region effect in edge, and the integration with the overall image seems not considered;\n3. As the evaluation is an important step in the agentic system, which direct the preference of the system. Therefore, the role of the evaluation metrics should be further investigated;\n4. It should better provide the restoration plan and agenda for exhibition;", "was_revised": true, "final_justification": "All of my issues have been solved, and I will maintain my score.", "rebuttal": "We sincerely thank Reviewer eAhT for highlighting the strengths of our work. We are glad that you found our agentic framework for 4K super-resolution systematic, well-structured, and practically relevant. Your recognition of our clear plan–execution–evaluation–reflection pipeline, the dedicated face restoration subsystem, and the flexibility we provide for user customization is greatly appreciated.\n\n> **W1: Provide the fast/slow option to balance the performance and process time**\n\n**A1**: **TL;DR: We develop a Fast4K mode for 4KAgent to balance the performance and process time.**\n\nThank you for pointing this out. We developed a Fast4K mode based on 4KAgent. Specifically, when the image resolution is higher than the resolution threshold s set by the user (longer side > s), 4KAgent will disable long-running methods in the toolbox in order to reduce the processing time of 4KAgent. At the same time, this resolution threshold s can be set by the user, so the user can flexibly adjust it to balance performance and processing time.\n\nBased on the Fast4K mode, we conduct an experiment on DIV4K-50 by setting different resolution threshold s (s = 1024 / 4096) in 4KAgent and evaluate the running time and performance. Experiment result is shown below (Running time is evaluated by RTX 4090 GPU):\n\n| Method           |  NIQE &#8595;   |   CLIPIQA &#8593;   |   MUSIQ &#8593;   |  MANIQA &#8593;  |  Avg. Running Time (s)  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|:-----------------:|\n| DiffBIR [1] (16x)  |   **2.65**  |    0.7078    |   38.59   |  0.5858  |       976.2          |\n| OSEDiff [2] (16x)  |   8.37  |    0.5680    |   25.07   |  0.4210  |       106.6          |\n| PiSA-SR [3] (16x)  |   9.30  |    0.5549    |   24.51   |  0.3861  |       107.8          |\n| **4KAgent** (s=1024) |    3.51     |    0.7310          |     **44.51**      |     0.5689     |        582.7         |\n| **4KAgent** (s=4096) |   3.15  |    **0.7585**    |   44.16   |  **0.5928**  |     1551.8      |\n\nIt shows that, simply set s to 1024, the average running time of 4KAgent decreases from 1551.8s to 582.7s (62.3% &#8595;) with minimal effect on performance. Under this setting, 4KAgent is even faster than the model-based approach (DiffBIR).\n\n> **W2: Unnatural region effect in edge for face restoration pipeline**\n\n**A2**: **TL;DR: The face restoration pipeline will introduce boundary artifacts in a low frequency of occurrence, and the benefits of the face restoration pipeline in 4KAgent outweigh the impact of the boundary artifacts it introduces.**\n\nThank you for pointing this out. When designing the face restoration pipeline in 4KAgent, we follow the ‘whole image restoration’ procedure from previous methods (e.g., GFPGAN [4], CodeFormer [5], DifFace [6]). Specifically, we use the toolbox in GFPGAN to extract faces in the image and paste them back after restoration. Therefore, it may introduce boundary artifacts at the edge. We conduct an experiment for this.\n\nSpecifically, we select 100 LQ images in the WebPhoto-Test dataset as test images. Then, we input these images to GFPGAN, CodeFormer, and DifFace for face restoration under the ‘whole image restoration’ procedure. We also collect result images from 4KAgent. After obtaining result images, we conduct a user study to explore the boundary artifacts. We define the boundary artifacts as: **Jagged edges or color halos along the face boundary**.  We ask users to evaluate the result images and count the occurrence of the boundary artifacts. Then we calculate the average occurrence of the boundary artifacts. Experiment results are shown below:\n\n| Method           |  GFPGAN   |   CodeFormer   |   DifFace  |  4KAgent |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|\n| Avg. boundary artifacts  |   9.75 / 100  |   8.75  / 100    |  25.75  / 100   |  7.75 / 100  |\n\nIt shows that SOTA face restoration methods and 4KAgent introduce boundary artifacts following the ‘whole image restoration’ procedure. However, the frequency of occurrence is low (less than 10%), and 4KAgent achieves the lowest frequency of occurrence with its system design.\n\nWe also conduct an ablation study of the face restoration pipeline. Specifically, we compare the result images from 4KAgent with different profiles (ExpSR-s4-P, ExpSRFR-s4-P) on the WebPhoto-Test dataset. The key difference between profiles and corresponding experiment results is shown below:\n\n| Method           |  Restore Option  |   Face Restore   |   NIQE &#8595;   |  CLIB-IQA &#8593;  |  DSL-FIQA &#8593;  |\n|----------------|:---------:|:--------------:|:-----------:|:----------:|:-----------------:|\n| 4KAgent (ExpSR-s4-P) |   super-resolution      |     False         |     5.11      |    0.6415      |       0.7194          |\n| 4KAgent (ExpSRFR-s4-P) |   super-resolution  |   True    |   4.53  |  0.6602  |     0.7237      |\n\nwhere ‘Face Restore’ controls the activation of the face restoration pipeline. It shows that when activating the face restoration pipeline, both the general image perceptual quality and face-related IQA metrics are enhanced. We also provide a visual comparison in Figure 8 in the Appendix, which further demonstrates the superiority of the face restoration pipeline by the enhanced facial and hair details from the image from 4KAgent by activating the face restoration pipeline.\n\nTherefore, following the previous procedure, the face restoration pipeline in 4KAgent will introduce boundary artifacts with a low frequency of occurrence. However, the face restoration pipeline will enhance the overall image quality and face quality. We believe that the benefits of the face restoration pipeline in 4KAgent outweigh the impact of the boundary artifacts it introduces. We will further modify the face restoration pipeline to reduce the boundary artifacts it introduces.\n\n> **W3: The role of the evaluation metrics should be further investigated**\n\n**A3**: Thank you for pointing this out. When designing the quality score used for evaluation in the 4KAgent system, we combined commonly used no-reference IQA metrics and HPSv2 scores (for human preferences). Experimental results show that 4KAgent achieves SOTA results, especially in terms of perceptual quality. In the future, we will further study the role of evaluation metrics, such as using block processing at ultra-high resolution to achieve more accurate evaluation results, and combining more advanced aesthetic scores.\n\n> **W4: Provide examples of restoration plan and agenda for exhibition**\n\n**A4**: Thank you for pointing this out. We show some examples here.\n\nFor the first image in Figure 1 with tag ‘4K Upscaling’, its restoration plan and restoration sequence is: Denoising@SwinIR(&#963;=50) &#8594; Super-resolution@DiffBIR &#8594; Super-resolution@SwinIR (Real-ISR).\n\nFor the lower left image in Figure 1 with tag ‘Human Faces’, its restoration plan and restoration sequence is: Super-resolution@OSEDiff &#8594; Denoising@Restormer &#8594; JPEG Compression Artifact Removal@FBCNN (BQF) &#8594; Defocus Deblurring@DiffPlugin &#8594; Super-resolution@DiffBIR. BQF in FBCNN indicated 'Blind to Quality Factor'.\n\nFor the left image in Figure 4, its restoration plan and restoration sequence is: Super-resolution@PiSA-SR &#8594; Dehazing@RIDCP.\n\nFor the right image in Figure 4, its restoration plan and restoration sequence is: Deraining@Restormer &#8594; Denoising@SwinIR(&#963;=15) &#8594; Super-resolution &#8594; OSEDiff.\n\nWe will show more examples of image restoration plans and the intermediate images produced by each step in the Appendix in the next version of paper.\n\n> **Q1: Observing the winning rate MoE to identify restoration models that suited for different domains**\n\n**A5**: Thank you for pointing this out. As we conduct experiments on images from different domains, we calculate the winning rate of SR methods from these experiments. The result is shown below:\n\n| Task                                 |      Benchmark (# number of images)      | Leading SR Tools (# number of selection) |\n|--------------------------------------|:----------------------------------------:|:-----------------------------------------:|\n| Real-World image SR (x4)             |      RealSR (100)    |   DiffBIR (55), PiSA-SR (22), OSEDiff (16)  |\n| AIGC SR (x4)                         |     GenAIBench (100)      |    DiffBIR (61), OSEDiff (25), PiSA-SR (14)    |\n| Remote Sensing SR (x4)              |    DOTA (183)     |   HMA (79), HAT-L (54), PiSA-SR(15)   |\n| Fluorescence Microscopic Image SR (x4) |     SR-CACO-2 (300)      |   DRCT (238), HMA (23), HAT-L (17)   |\n| Pathology Image SR (x4)             |    bcSR (200)  |   X-Restormer (84), HAT-L (64), HMA (30)    |\n| Medical Image SR (x4)               |    Chest X-ray 2017 (624)   |  HMA (312), DRCT (171), X-Restormer (73)    |\n\nIt shows that when facing images from different domains, different SR methods win from the MoE policy. We believe that the results obtained by analyzing the winning rate can provide some insights into model selection for SR tasks in different fields of images.\n\n\n**Reference**\n\n[1] Lin, X., He, J., Chen, Z., Lyu, Z., Dai, B., Yu, F., ... & Dong, C. (2024). Diffbir: Toward blind image restoration with generative diffusion prior. ECCV.\n\n[2] Wu, R., Sun, L., Ma, Z., & Zhang, L. (2024). One-step effective diffusion network for real-world image super-resolution. NeurIPS.\n\n[3] Sun, L., Wu, R., Ma, Z., Liu, S., Yi, Q., & Zhang, L. (2025). Pixel-level and semantic-level adjustable super-resolution: A dual-lora approach. CVPR.\n\n[4] Wang, X., Li, Y., Zhang, H., & Shan, Y. (2021). Towards real-world blind face restoration with generative facial prior. CVPR.\n\n[5] Zhou, S., Chan, K., Li, C., & Loy, C. C. (2022). Towards robust blind face restoration with codebook lookup transformer. NeurIPS.\n\n[6] Yue, Z., & Loy, C. C. (2024). Difface: Blind face restoration with diffused error contraction. TPAMI."}]

Author rebuttal:

Meta-review: This paper presents the very first attempt for an agentic framework that integrates tools and methods for multi-degradation real-world super-resolution into a unified system that is highly customizable. After rebuttal 3 out of 4 reviewers are positive about the paper increasing their confidence and rating (for 1 reviewer) since the authors' rebuttal seems to be effective in addressing their points raised. Only 1 reviewer (wkHw) did not change their score which remained negative but although they raised reasonable points for discussions, they failed to engage in further discussion following the authors' very detailed responses. Overall, the consensus for this paper seems rather positive, hence accept
Decision: Accept (poster)