SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused Text-Conditioning
Authors: Chen Chen
Majid Abdolshah
Violetta Shevchenko
Hongdong Li
Chang Xu
Pulak Purkait
Venue: NeurIPS 2025 poster ()
Keywords: Real-World Image Super-Resolution
Diffusion Models
Attention Mechanism
Primary area: applications

Abstract: Existing diffusion-based super-resolution approaches often exhibit semantic ambiguities due to inaccuracies and incompleteness in their text conditioning, coupled with the inherent tendency for cross-attention to divert towards irrelevant pixels. These limitations can lead to semantic misalignment and hallucinated details in the generated high-resolution outputs. To address these, we propose a novel, plug-and-play *spatially re-focused super-resolution (SRSR)* framework that consists of two core components: first, we introduce Spatially Re-focused Cross-Attention (SRCA), which refines text conditioning at inference time by applying visually-grounded segmentation masks to guide cross-attention. Second, we introduce a Spatially Targeted Classifier-Free Guidance (STCFG) mechanism that selectively bypasses text influences on ungrounded pixels to prevent hallucinations. Extensive experiments on both synthetic and real-world datasets demonstrate that SRSR consistently outperforms seven state-of-the-art baselines in standard fidelity metrics (PSNR and SSIM) across all datasets, and in perceptual quality measures (LPIPS and DISTS) on two real-world benchmarks, underscoring its effectiveness in achieving both high semantic fidelity and perceptual quality in super-resolution.

SRSR: Enhancing Semantic Accuracy in Real-World Image Super-Resolution with Spatially Re-Focused
Text-Conditioning
Chen Chen 1.2+
Majid Abdolshah*
Violetta Shevchenko*
Hongdong LI3
Chang Xu²
Pulak Purkait"
Amazon
The University of Sydney
AAustralian National University
Abstract
Existing diffusion- baausd super-resolution approaches often exhibit semantic ambi- guities due to inaccuracies and incompleteness in their text conditioning, coupled with the inherent tendency for cross-attention to divert towards irrelevant pixels. These limitations can lead to semantic misalignment and hallucinated details the generated high-resolution outputs. To address these, we propose novel, plug- and-play spatially re-focused super-resolution (SRSR) framework that consists two core components: first, we introduce Spatially Re-focused Cross-Attention (SRCA), which refines text conditioning at inference time by applying visually- grounded segmentation masks to guide cross- attssnnt Second, introduce a Spatially Targeted Classifier- Free Guidance (STCFG) mechanism that selectively bypasses influences ungrounded pixels prevent hallucinations. Extensive experiments on both synthetic and real-world datasets demonstrate that SRSR consistently outperforms seven state-of-the-ar baselines in standard fidelity met- rics (PSNR and SSIM) across all datasets, and in perceptual quality measures (LPIPS and DISTS) on real-world benchmarks, underscoring its effectiveness in achieving both high semantic fidelity and perceptual quality in super-resolution.
Introduction
Image super-resolution (SR) aims to restore a high-resolution (HR) image from a low-resolution (LR) counterpart degraded by blur, noise, and compression artifacts other distortions. This classic problem has broad real-world applications, ranging from photography [18 and digital surveillance [63]
32 to medical imaging [12 17) and remote sensing (45). However, restoring both photorealistic details and semantically coherent content under severe degradations remains an open challenge. Early deep-learning-base based approaches often assume simple, known degradations (c.g., bicubic or Gaussian downsampling) and investigate new architectural designs 4.4 8 9 24 28 38 67]. Subsequently, super-resolution methods based on generative models like GANs I and diffusion models [16] 29]
can produce realistic details 62 46, 19 49), yet they frequently struggle on real-world LR image inputs that deviate from the above assumptions, resulting in clear artifacts in the super-resolved outputs. Consequently. recent research adopts more complex and realistic degradation pipelines (e.g., Real-ESRGAN (46)) and exploits the powerful Stable Diffusion (SD) prior (36), as in StableSR [44 and DIffBIR (25), to improve generalization in real-world ISR. However, these methods overlook textual guidance, thus risking semantic misalignment.
recent trend is to incorporate text priors to guide super-resolution via text-to-image Stable Diffusion pipeline, leading to better semantic fidelity (58)
59 31 54 53). Among these methods,
the degradation-aween aware prompt extractor (DAPE) proposed by SceSR [54] demonstrates superior
Work done during internship at Amazon.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Groundtrach
SerSR
Original attention
Graud-trrch
SeeSR
Original attention
'bird'
"stone"
'grasss"
Zoomed LR
Ours
Re-focused attention
Zoomed LR
Ours
Re-focused attention
'bird' "stone"
"stinee 'boon'
Figure 1: Illustrations of how inherent crossatteention can be misled by irrelevant tokens, resulting in semantically incorrect restorations (top). Left: the baseline mistakenly associates the stone region with the token 'bird", and the animal's neck and beak with 'stone', producing wing-like artifacts the stone and unnatural textures on the animal. Right: attention for 'stare' is scattered across irrelevant patches, and both the eye and lion face incorrectly respond to 'grass', introducing hallucinated textures. We propose re- re-focusing cross-attention by constraining the influence of each text token to its grounded region, yielding sharper and semantically aligned reconstructions (bottom).
performance in terms of both degradation-awarenn and efficiency compared to other off-the-shelf models used like ResNet [14], YOLO [3), BLIP 2 and LLaVA (26] employed in other works. OSEDiff 53
further trains a one-step model that also leverages DAPE as the prompt extractor.
Despite these advances, text conditioning can induce hallucinations, causing ambiguous semantics in the restored results, as illustrated in Fig. [ We identify three main limitations in current designs: Firstly, existing approaches rely exclusively on cross-attention to inject text prompts, but tokens often leak onto unrelated regions, leading mismatched guidance and semantic ambiguity. Secondly, although DAPE is more degradation- robust severe degradations, making accurate prompt extraction challenging. Providing erroncous text prompts can degrade the fidelity of super-resolved outputs more severely than omitting text guidance altogether. Thirdly, prompt extraction methods not guarantee full-image coverage, leaving ungrounded regions without guidance from relevant semantics and vulnerable to the influence of unrelated text prompts. recent work 440 exploits Mask2Former [5] trained on ADE20K (70) to achieve full-image coverage by assigning dense segmentation labels to every pixel. However, its segmentation model lacks degradation-awareneee and restricted to 150 categories, limiting its scope for weal-world applications that demand open- ppenbvocabylary understanding. Bridging these limitations to achieve fully coherent, semantically correct super-resolution remains an open challenge.
than other prompt extractors, it remains only partially
To address the aforementioned challenges, we propose novel framework that conducts Spatially Re-focused Resol--nso (SRSR). We first apply visual grounding filter less relevant tags and generate tag-region pairs via segmentation masks. Based on these, we introduce Spatially Re-focused Cross-Artention (SRCA), which constrains each tag's influence to its corresponding spatial region, effectively mitigating semantic hallucinations from the first two limitations. To further improve restoration in ungrounded regions, we introduce a Spatially Targeted Classifier-Free Guidance (STCFG) mechanism that selectively disables classifier-free guidance in these regions to avoid undesired eext-conditioning effects. Notably, our method operates exclusively at inference time, eliminating the need for additional training or fine-tuning. As result, it serves as lightweight alug-and-play module compatible with any cross-attention-bee
SR approach that utilizes text priors.
Our results show that incorporating SRSR into both the 1-step OSEDiff and the 50-step SeeSR base- lines yields significant improvements in full-reference metrics across synthetic and real-world datasets. When compared to seven other state-of- sfate-of-the-att the-art baselines, our method consistently outperforms all alternatives in fidelity measures PSNR and SSIM on every dataset, and achieves the top performance for perceptual metrics LPIPS and DISTS on the two real-world datasets (RealSR and DrealSR) while ranking second on the synthetic DIV2K set. summary, our contributions are threefold: First, we highlight underexplored gaps in existing methods that impede semantically accurate super-resolution. Second, we propose novel SRSR framework that effectively alleviates semantic ambiguity through spatially re-focused cross-attes aatenttttnnion (SRCA) and spatially targeted CFG (STCFG), while remaining lightweight, inference-time wong-and-ply module for any cesss-attentionnbaae SR approach with text priors. Third, SRSR achieves new state-of-thee performance against seven strong basclines.

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2 Related work
2.1 Non-diffusion- super-resolution methods
Starting with CNN-based approaches, SR has been extensively explored through models that directly map LR images to HR outputs in an end-to-end manner [21] 69
1
9 39 6.. Many CNN-based
methods focus on designing deeper or wider networks and improving attention mechanisms to enhance SR performance [48] With the rise of generative models, GANs have gained popularity by using adversarial frameworks where the generator creates HR images closely matching real image distributions, thereby improving perceptual quality and realism. Real-ESRGAN (46) advanced this by introducing high-order degradation model and modifying the U-Net discriminator to enhance stability and visual performance in real-world SR scenarios. Building on this, uncertainty and 30]. Uncertainty-Awar GANs integrated pixel-level uncertainty into adversarial training for fine-grained feedback, while SeD incorporated semantic information into the discriminator guide the generator in producing semantically aligned textures. CAL-GAN [6], another context-aware method, employs a mixture of classifiers handle image patches based on content, improving discriminator capacity for photo- realistic SR. In parallel, SFT-GAN (47 demonstrates how spatially-varying categorical priors derived from semantic segmentation maps can be integrated into SR networks through Spatial Feature Transform (SFT) layers, enabling region-specific modulation of features and the recovery of textures that are more realistic and faithful to underlying semantics. However, these methods primarily focus on broader image-level semantic context rather than explicitly leveraging object-level understanding within images for SR.
semantic-aware approaches have been proposed 27
2
22
61
27
22
2.2 Diffusion- ceefusion-based super-resolution methods
DDRM and DDNM 19 49 were the earlier efforts that develop DDPM-based SR methods. Subse- quently,
based SR methods like StableSR 4 and DiffBIR 225 were proposed to tackle the more challenging real-world image super-resolution (Real-ISR) task by leveraging Stable Diffusion (SD)'s 's strong prior that trained using billions of image-text pairs. A recent trend is to incorporate text priors to guide generation in text-to-image SD, leading to better semantic fidelity. For example, CoSeR (37 highlights the potential of bridging image and language understanding using diffusion-based priors and cognitive embeddings. Also, PASD [58] extracts text prompts via off-the-shelf models (ResNet [14], YOLO (33 and BLIP (23) and leverages ControlNet (65)
without modifying SD's pre-trained weights. SUPIR 55) and XPSR [3] employ LLaVA (26).
while SeeSR (54) proposes a degradation-aware prompt extractor (DAPE) that improves on the Recognize Anything Model (RAM) (68] for LR images. OSEDiff (53 further trains one-step
model that also leverages DAPE as the prompt extractor, demonstrating its superiority over larger lssion-language models like LLaVA. Most recent works like SegSR (55) and HolisDiP [40] propose to additionally use semantic segmentation models to extract prompts with more comprehensive coverage. Despite these advances, ensuring the accuracy of the extracted prompt and the robustness of its cross-attention injection remains difficult under heavy degradation, as incorrect text guidance can still trigger hallucinations and semantic ambiguity in the restored outputs.
3 Method
3.1
Research gaps
Recent work has shown that super-resolution (SR) methods based on Stable Diffusion (SD) hold promise, primarily due SD's powerful prior learned from billions of image-text pairs. However, when operating on degraded LR images, semantic errors can arise, leading to inaccurate reconstruc-
tions. Recent studies [58 54 59
53 have demonstrated that leveraging SD's text-conditioning, SR results. Nevertheless, hallucination is often observed due to three major limitations of the current designs:
guided by text priors extracted from LR images, can yield more semantically
Diverted cross-attention. Existing approaches rely on cross-attention to implicitly associate text prompts with image pixels. However, we observe that these associations are often misaligned, with text tokens attending to irrelevant regions. As shown in Fig. (top row), the baseline cross-attention maps frequently misattribute tokens such as 'bird', 'stone". "stare". and 'grass' to unrelated image areas, leading to semantic ambiguity and perceptual artifacts. The output's 's semantic fidelity is further degraded when the extracted text contains incorrect or loosely related tags. For instance, in Fig. Fig. (top row), 'tower' is loosely relevant to 'building', but the latter is mainly used for guiding the restoration of the building regions. In contrast, 'tower' pays significant attention to regions highlighted by the yellow bounding boxes, which actually correspond to tree-like structures, introducing hallucinated
3

---

Ground-tuuth
Ungrounded
Original attention
'building"
'water"
'lwwr'
Zoomed LR
Oury
Ungrounded
Re-focused atlention
'huilding"
'watee'
"onwer"
Ground-rrath
SeeSR
Ungrounded
Original attention
"gun"
'cumouflage"
Zoaned LR
Own
Ungronnded
Re-focurse atlention
'gun"
'mon" 'camouflage"
Figure 2: Analysis of how our proposed Spatially Re-focused Cross-Artention (SRCA) and Spatially Targeted Classifier-Fre Guidance (STCFG) improve semantic fidelity in text-conditioned super- resolution. The Ungrounded mask highlights regions where textual tag be confidently grounded. Existing methods rely solely on inherent cross-attention, allowing global or irrelevant tokens influence all regions. SRCA addresses this by limiting each token's influence to its corresponding grounded region only, reducing semantic confusion. However, this leaves ungrounded regions only associated with global tokens (e.g., EOS, padding, punctuation), where summary tokens like EOS can still carry semantics of the entire prompt and influence its restoration. To resolve this, STCFG disables text conditioning entirely in ungrounded areas by using unconditional noise prediction in the reverse diffusion process, further enhancing the ungrounded region's 's restoration.
content where it does not belong. similar issue is shown in Fig. (third row), where the incorrect tag 'camouflage" is extracted by DAPE from the degraded LR image. As a result, the SeeSR baseline synthesizes camouflage-like patterns in regions (highlighted in red boxes) that should depict stones. Although the output appears visually plausible, it suffers from poor semantic fidelity.
Incorrect prompts. Extracting accurate text prompts from LR images remains challenging. Recent efforts, such as the degradation-aware prompt extractor (DAPE) (54), improve on the Recognize Anything Model (RAM) but still produce misguiding prompts under severe degradation. Injecting these incorrect signals can lead to even more erroneous SR results than using no text guidance at all.
Incomplete prompts. While DAPE demonstrates greater robustness to image degradations than many alternative text extractors, it does not guarantee comprehensive coverage of the entire image. It may miss salient objects and often fails assign tags to non-object background regions due to its object-centric design. As a result, uncovered regions encounter two key issues: (1) they lack the semantic guidance that tagged regions benefit from, and (2) they remain susceptible to influence from unrelated text prompts, leading to hallucinations in areas that should remain unaffected.
In response, we propose novel Sputially Re-focused Super- Resolutio (SRSR) framework (Fig. that comprises two key components. First, the Spatially Re-focused Cross-Aftention (SRCA) module (Sec. 3.2) tackles the first two gaps by mitigating semantic ambiguity and reducing the influence of

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LR Emage
'heod'
pyngsin
'sionc"
'ydlow'
Ungrounded Mask
Grounded SAM
Spatially Re-Focdsed Cross Acteation (SRCA)
Ca Eayur
CA Laver
CA Layer
Dillusiaa Network
CA
Extracted Tape
Deal, bird, binoh, head,, pangrin, stons, stoned) yellow
Image
Figure 3: Overview of our proposed pipeline. First, the LR image processed by a Degradation- Aware Prompt Extractor (DAPE) [54] to obtain text tags. Both the LR image and the extracted tags are then passed Grounded SAM, which produces visually grounded tag-mask pairs. We also define an ungrounded mask as the complement of the union of all grounded masks. Next, each tag mig-mask pair integrated into 16 U-Net cross-attention layers, using the masks to constrain text conditioning precisely to relevant regions. Once noise prediction is complete, we selectively apply Classifier-Fires Free Guidance (CFG) to grounded pixels while leaving ungrounded pixels under unconditional guidance, ensuring they remain unaffected by the text prompts. After T denoising steps, final decoder maps the latent representation back to pixel space, yielding the super-resolved (SR) image.
incorrect or loosely related tags through spatially constrained attention. Second, the Spatially Targeted Clasiffie--Free Guidance (STCFG) module (Sec. 3.3 targets the third limitation by improving restoration in regions lacking explicit grounding. Notably, SRSR designed as an efficient, inference- only solution that requires additional training fine-tuning. making it compatible as a plug-and play module with any cross- cotss--atention-aa based super-resolution method that utilizes text priors.
3.2 Spatially Re-focused Cross-Attention (SRCA)
First, ground the DAPE-extracted tags onto the image using Grounded SAM [35], assigning each tag to a specific region. This step offers two key benefits: (1) tags that cannot be confidently grounded likely irrelevant and thus removed, avoiding the injection of erroneous signals into the super- resolution process, addressing the aforementioned second limitation; (2) each grounded tag is now accompanied by segmentation mask, enabling our proposed Spatially Re-focused Cross-Artention (SRCA) mechanism to mitigate semantic ambiguity and address the aforementioned first limitation.
With these text-mask pairs, SRCA refines the spatial precision of text conditioning, effectively reducing semantic ambiguity in the super-resolved outputs. Specifically, instead only relying on the implicit cross-attention itself, we explicitly leverage each tag's corresponding segmentation mask to localize and re-focus the attention of text-conditioning, effectively suppressing attention outside the designated target regions. This ensures that each text token attends exclusively to its intended image regions, mitigating distractions from irrelevant areas. See Fig. T [and] for visual analysis.
Formally, the cross-attentio mechanism computes attention weights aij for pixel i and token as:
Osj = SoftMax @ K
where Q and K are query and key embeddings, respectively, and d is the feature dimension. These weights form a weighted sum of the token values V, to obtain O, which the output for pixel
O, Σ aij Vj+
(2)
5

---

In SRCA, we first apply a binary mask Mi (1 for valid regions) to refine the attention weights atj:
SRCA
Maj Oaj-
We then re-normalize these masked weights across both pixel and token dimensions:
SRCA
SRCA
SRCA oz
This preserves proper attention distribution and ensures that zeroed-out entries do not distort the attention map while preserving the normalization of valid tokens, ensuring they continue to sum to one. As a result, this re- ro-focusing operation suppresses contributions from irrelevant tokens, ensuring that each pixel i attends only to the relevant tokens while maintaining consistent overall attention magnitudes. For example, for pixel i, relevant token V', would correspond to Mpn = 1, resulting in the attention weight unchanged SRSR = Oaj- Conversely, for an irrelevant token V,, the segmentation mask would have M.. 0, resulting in no attention is placed
such token. Importantly, as Eq. 2 computes weighted average of all tokens, eliminating such irrelevant token also helps increase the attentions to the relevant tokens. Consequently, SRCA produces more contextually aligned super-resolution results, with enhanced focus on the key regions of the image.
3.3 Spatially Targeted CFG application (STCFG)
Although our proposed SRCA in Sec. 3.2 addresses the first two limitations, the third limitation of insufficient tag coverage remains. Moreover, when our grounding mechanism is overly conservative and discards tags that are partially relevant, coverage can be reduced further. Resolving this issue is crucial for improving our overall performance. A recent work [40] proposed an interesting approach to address this challenge by employing Mask2Former [5), trained ADE20K [70], to generate semantic segmentation labels for each image region, which are then used as text conditions. However, unlike DAPE, 2ask2Former is not degradation-avee aware and often produces inaccurate labels when applied to degraded LR images, failing to resolve the second limitation and ultimately resulting in inferior performance. Furthermore, the model trained on ADE20K is limited fixed set of 150 categories, which restricts its expressiveness and precision in practice. Through additional ablation studies (Tab. (2) we observe that using these additional segmentation tags in the prompt leads to degraded performance. We also tested open-vocabulary segmentation methods, such as Prompt-Frre Anything Detection and Segmentation in DINO-X (34), and observed that while they outperformed Mask2Former, their lack of degradation-awarenee similarly led to inaccurate tags and inferior results compared to using DAPE alone. These indicate that inaccuracies are more harmful than incomplete coverage for text conditionings. More discussions are available in the ablation studies (Sec. (43).
Given the inherent difficulty and threshold sensitivity in balancing accuracy and coverage of tags extracted from degraded LR images, we note that the coverage limitation affects only the ungrounded regions. Instead of fine-tuning thresholds, can we explore alternative that directly improves ungrounded-region quality by design without risking incorrect prompts? Drawing from our insight that erroneous tags more detrimental than no tags, we argue that omitting guidance is preferable to providing incorrect conditioning. In standard CFG, rext-conditional output eø y) is balanced against an unconditional output c0( Φ). The combined prediction é typically computed as:
t co(2,, Φ) + s [<ooITe y) - co( })
(5)
where 8 2 is the guidance scale, indicating positive weight is applied to the text-conditional generation, while non-positive weight is assigned the unconditional generation. However, uniformly applying CFG can degrade ungrounded regions because they only receive prompts from semantic-free or summary global tokens (c.g., EOS, padding, punctuation), where summary tokens like EOS can still carry semantics of the entire prompt and influence its restoration. To address this, we construct an ungrounded mask M for each image by taking the complement of the union of all grounded segmentation masks (i.c., M = 1 for ungrounded pixels and 0 for grounded pixels). We then selectively apply CFG only to grounded pixels, while leaving the ungrounded pixels unconditional:
é + (1 - M.) co(2,, の) + (co(x,,)) y) - co(2,, o))] + M, en (Zx,,,)) d),
(6)
where M; E 10 indicates whether pixel i is ungrounded. This targeted approach ensures that uncertain or irrelevant tags do not influence the restoration of ungrounded regions, while grounded pixels still benefit from text conditioning, thereby preserving the visual fidelity.

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Table 1: Quantitative comparison with state-of-the-art methods both synthetic and real-world
baselines. 's' denotes the number of diffusion reverse steps. Red = best, Blue = second-best.
Dataset
Method
PSNRT
SSIM
I.PISS]
DISTS
FIDL
Metric
NIQEL
MUSIQU
MANIQAT
CLIPIQI
StableSR =200
24.70
0.7085
0,3018
0.2288
128.51
5,9122
0.6221
0.6178
DifEBRR-s50
24.75
0.6567
0,3636
0.2312
128.99
5,5346
64.98
0.6246
0.6463
PASD.20
25.21
0,3380
0.2260
124,29
5.4137
0.6487
0.6620
ResShift-s15
26.31
0.7421
0.3460
0.2498
135.93
7.2635
58.43
0,5285
0.5444
RealSR
SinSR-11
26.28
0,3188
0.2353
131,93
6.2872
0.5385
0.6122
OSEDifi-s
24.43
0.7153
0.3173
0.2363
125,97
6.3897
67.52
0.6168
0.6742
SocSR-450
28.18
0.7216
0.3009
0.2223
125.55
5,4081
69.77
0.6442
0.6612
DASR-OSEDiff-st s1
24.53
0.7206
0.3166
0.2378
132.55
6.6106
67.24
0.6137
0.6710
SRSR-Se:SR-550
26.40
0.7632
0.2718
0.2092
126.31
5.8627
62.88
0,5628
0,5409
StableSR-4200
23.26
0.5726
0,3113
0.2048
24.44
4.7581
65.92
0.6192
0.6771
DifIBRR-x50
23.64
0.5647
0.3524
0.2128
30,72
4.7042
65.81
0.6210
0.6704
PASDD<20
23.14
0.5505
0.3571
0,2207
29.20
4.3617
68.95
0.6483
0.6788
ResShift-s15
24.65
0.6181
0,3349
0.2213
36.11
6.8212
61.09
0.5454
0.6071
Dal
SinSR=x1
24.41
0.6018
0.3240
0,2066
35.57
6,0159
62.82
0.5386
0.6471
OSEDiff-sl
23.31
0.5970
0.3046
0.2129
26.80
5,4031
65.56
0.5857
0.6588
SeeSR <50
23.68
0.6043
0.3194
0.1968
25.90
4.8102
68.67
0.6240
0.6936
SKSR-OSEDiff-s
23.43
0.6023
0,3053
0.2142
27.65
5,5389
65.08
0.3797
0.6531
SRSR-Sce5R-550
24.72
0.6416
0.3275
0.1991
25.31
5.4986
0,5402
0,5518
StableSR=2200
28:03
0.7536
0.3284
0.2269
148.98
6.5239
58.51
0,5601
0.6356
Dif'BRR
26.71
0.4557
0,2748
166.79
6.3124
0.5930
0.6395
PASD->20
27.36
0.7073
0.3760
0.2531
156.13
5,5474
64.87
0.6169
0.6808
ResShift-s15
28.46
0.4006
0.2656
172.26
8.1249
0.4586
0,5342
DrealSR
SinSR-sl
28.36
0.7515
0.3665
0.2685
170.57
6,9907
55.33
0.4884
0,6383
OSEDiffss
27.65
0.3177
0,2366
141.96
7,3050
0.5758
0.7060
SceSR-s50
28.17
0.7691
0.3189
0.2315
147.39
6.3967
64.93
0.6042
0,6804
SRSR-OSEDifE-sl
27.72
0.7781
0.3178
0.2379
139.66
7.5271
63.62
0.5736
0.7010
SecR-Soc
29.50
0.8128
0.2866
0.2176
146.98
7.1279
54.04
0.4962
0,5513
Visually, as shown in Fig. SRCA and STCFG play complementary roles. SRCA improves the semantic fidelity of grounded objects by refining the cross-attention maps. In the top example, the SeeSR baseline mistakenly restores part of the building (highlighted in red) water-like due to attention leakage from the 'water' token. SRCA re-focuses the attention of both 'building' and 'water' tokens to their correct regions, leading to the accurate restoration of the entire building. However, SRCA falls short in suppressing the influence of global tokens on ungrounded regions (as visualized by the ungrounded masks). To address this, STCFG applies unconditional guidance to those regions, effectively preventing hallucinations and yielding more faithful restorations, as qualitatively observed.
4
Experimental results
4.1 Setup
that require training, our proposed SRSR operates purely at inference time and therefore only needs test datasets. Following the baselines, we adopt the standard test set from StableSR [4], which includes both synthetic and real-world data. The synthetic portion contains 3,000 images x512), whose ground truths (GT) are randomly cropped from DIV2K- Val [I] and degraded via the Real-ESRGAN pipeline [46]. The real-world portion
Datasets. Unlike prior approaches 44 25
58
54 53
comprises LQ-HQ pairs from RealSR 2 (128x 128) and DRealSR [52] (512x512), respectively.
Baseline methods. SRSR is a plug-and-play module that integrates seamlessly into any cross- based SR approach utilizing text priors, offering performance gains without additional training. To validate its effectiveness, we incorporate SRSR into two state-of-the-ar methods: a 50-step method, SeeSR [54], and single-step method, OSEDiff (33. Users can select between these baselines based on their preferred trade-off between efficiency and quality, since multi-step approaches often yield better results at the expense of increased computation.
We further compare our SRSR-SceSE and OSER-f variants against a range of strong baselines under their standard configurations, including StableSR 44] DifIBIR 225. PASD (58), ResShift (60), and SinSR [50]. Among these, ResShift trains a diffusion model from scratch in the pixel space, while SinSR is a one-step distilled model derived from ResShift. The remaining methods operate in latent space and build on top of the pre-trained SD model.
Evaluation metrics. In line with prior work, we evaluate using both full-reference and no-reference metrics. For full-reference evaluation, we use PSNR and SSIM (11 as fidelity measures, and LPIPS (66 and DISTS 7 to assess perceptual quality. FID [5] is also employed to gauge the distributional distance between super-resolved and ground-truth high-resolution (HR) images. For no- reference assessment, we adopt NIQE [64], MANIQA-pipal [57], MUSIQ [20), and CLIPIQA (3).
7

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Ground-truth image
SccSR result w/ hallucinations
Ours resalt w/o hallucinations
Full-rderence
Full Full-reference
PSNR:
SSIM: 0,0000
SSIM: 0.6681
LPIPS: 0.3355
LPIPS: 0.3534
DISTS: 0.115:0.1965
DISTS: 0.1897
No-reference
No-reference
NIJE: 3.10
NIGE: 5.02
MUSIQQ
MUSIQQ 55.41
MANIQA:
MANIQA:
CI CUIPIA:: 0.7226
CLIPIQA: 0,6150
PSNR: 27.75
PSNR 20.33
SSIM: 0.7541
SSIM: 0.7786
LPIPS:
No-reference NIQE:
No-reference MUSIQ
MUSIQ
MANIQA: 0.4928
MANIQA: 0,3691
CLIPIQA:
CLIPIOA:
Full-ederence
Full-reference PSNR: 23,53
PSNR: 22.75
LPIPS: 0.3513
LPIPS: 0.3015
DISTS: DISTS:0.1983 0.1983
DISTS: 0.1629
No-reference
No-eeference
NIQE: 3.23
NIQE: 4.10
MUSIQ: 76.37
MUSIQ 75.67
MANIQA: 0.6152
MANIQA: 0.5943
CLIPIQA: 0,5466
CLIPIQA: 0.5845
Figure 4: Qualitative results show that the baseline method exhibits clear semantic hallucinations. In contrast, plugging SRSR into the baseline leads to semantically faithful restorations. All full- reference metrics, including both fidelity (PSNR and SSIM) and perceptual quality (LPIPS and DISTS) measures, consistently validate the improvements brought by our method. However, is worth noting that the no-reference metrics tend to misjudge and heavily reward hallucinated results due to their design, as indicated by the significant performance gap observed despite degraded semantic realism. This exposes their limitations in assessing semantic fidelity for super-resolution.
Implementation details. We adopt DAPE [54 as our prompt extractor, following its use in both SeeSR and OSEDifI. DAPE excels handling degraded LR images while being more efficient than advanced multimodal vision-language models [26]. Although we experimented with various semantic segmentation models (e.g.. Former [5] and Prompt-Free Anything Detection/Segmenttaiie in DINO-X (34), they did not make it into our final SRSR design. For grounding, we employ Grounded SAM which is combined framework built upon DINO-X and SAM 2.
Complexity analysis. Within our proposed SRSR framework, both SRCA and STCFG are applied only during inference and modify the cross-attention and CFG processes dynamically without introducing any new learnable parameters. Our approach uses the same pretrained diffusion model and UNet architecture as the baseline, so the parameter count remains the same. Therefore, the performance gains are achieved without increasing model complexity or adding new parameters.
Efficiency analysis. Our integration of Grounded SAM into the pipeline is highly efficient. First, it requires only single inference per image before the SR process to pre-compute the masks, using the low-resolution (LR) input. The resulting masks repeated computation. Second, since only the LR image (e.g., 128x1 18)) is fed into Grounded SAM, the processing time is minimal (just 0. 12s per image) V100, and even for 512x512 inputs, remains low at 16,,,,, making our approach practical and scalable for real-world applications. This pre-processing step also does not affect the parameter count inference complexity.
cached and reused during inference, avoiding
4.2 Comparisons with state-of-the-art baselines
Tab. compares our methods against several of-te-ff baselines. To highlight SRSR's contri- bution, note how it improves given baseline: for instance, applying SRSR to SeeSR considerably boosts full-reference fidelity measures PSNR and SSIM across all three datasets. also enhances the full-reference perceptual quality metrics LPIPS and DISTS on two real-world sets (RealSR and DRealSR) while offering comparable performance on synthetic data (DIV2K-VA) Val).. No-reference metrics, however, often favor strong semantic cues, even if they are inaccurate, where it is frequently observed that hallucinated restorations have high performance them less reliable. Examples can be observed from Fig. 4 We also evaluate SRSR OSEDiff,
no-reference metrics, making

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which differs from SeeSR in two key aspects: it is one-step inference method (confirming SRSR's s flexibility regarding inference steps) and does not employ classifier free guidance (CFG) (making STCFG inapplicable). In this setting, only the SRCA module is integrated. Despite this, we observe consistent improvements in fidelity metrics (PSNR and SSIM) across all datasets, with only marginal trade-off's in perceptual quality (LPIPS and DISTS), demonstrating SRCA's standalone effectiveness in enhancing semantic accuracy. This observation is further supported by the comparison between V2 and V3 in Tab. 2 which highlights the standalone effectiveness of SRCA without STCFG, The slight drop in perceptual quality arises from ungrounded regions still receiving text-conditioning from summary or meaningless tokens (e.g., EOS, punctuation), which STCFG is designed to address. This indicates that while SRSR is broadly compatible with diverse frameworks, its full potential is best realized when the underlying method supports CFG. Nonetheless, using SRCA alone is valuable in common scenarios where semantic fidelity is prioritized over background perceptual quality. These results highlight SRCA's independent benefits and STCFG's complementary role.
Beyond direct baselines, we have also compare with a range of strong competitors, where our methods generally achieve the best performance full-reference fidelity and perceptual metrics, falling short of the top rank only in LPIPS and DISTS on DIV2K- Val, yet still securing second place in such cases.
4.3 Ablation studies
Grounding. As discussed in Sec. 3.2 and Sec. grounding increases tag accuracy by discarding irrelevant text at the expense of reduced coverage when certain relevant tags are mistakenly removed. This reflects accuracy typically outweighs completeness: providing fewer but more relevant tags produces better results than including extra but less reliable ones. For instance, comparing V1 and V2 in Tab. 2 shows that simply applying grounding (V2) to the SeeSR baseline yields small gains in three out of four metrics. Moreover, V5 differs from final V4 only by including ungrounded tags, which lowers performance on all four metrics. Additionally, grounding's 's effectiveness depends on its confidence threshold, which balances strictness (accuracy) against coverage (completeness). We further explore this trade-off in Sec. 4.4 Finally, it is also worth emphasizing that grounding does more than just manage the accuracy wwwuaaccccoollteeeess. trade-off: it also enables spatial re-focusing through SRCA, whereby each grounded tag is paired with segmentation mask to alleviate semantic ambiguity.
eecuracy-completemee
trade-off in tag extraction. Experimentally,
find that
Spatially Re-focused Cross-Attention (SRCA). SRCA mitigates semantic ambiguities and enhances output fidelity. When used alone without STCFG (V2 vs. V3), it improves fidelity at a slight cost to perceptual quality. However, when combined with STCFG (V4 vs. V6), SRCA yields improvements across all four metrics, underscoring its importance and complementary role within the full framework.
Spatially Targeted CFG application (STCFG). STCFG provides direct mechanism for enhancing ungrounded regions and significantly improves all four metrics, whether used alone (V2 vs. V6), in combination with SRCA (V3 vs. V4), in combination with both SRCA and different semantic segmentation methods (V9 vs. V7 and V10 vs. V8), demonstrating its effectiveness and versatility.
DAPE vs. Semantic Segmentation. As mentioned in Sec. 3.3 a contemporary work uses semantic segmentation (SS), specifically, Mask2Former (5) pre-trained on ADE20K, to extract semantic labels with complete image coverage. Motivated by their interesting work, we also experimented with
Table Ablation study evaluating the contribution of each component within SRSR. SRCA denotes spatially re-focused cross-attention, and STCFG denotes spatially targeted classifier-free guidance. Mask2Former and DINO-X represent difffrrntt semantic segmentation backbones. Bold indicates the best performance, Refer to Tab. for for the extended ablation results with no-reference metrics.
Version
PSNR
SSIM
LPIPS
DISTS]
VI: SeeSR
25.1717
0.7219
0.3008
0.2223
V2: SeeSR+ Grounding
25.1751
0.7234
0.3001
0.2229
V3: SecSR+
Grounding + SRCA
25.2688
0.7280
0.3013
0.2254
V4: SecSR+
Grounding SRCA STCFG (Ours)
26.3996
0.7632
0.2718
0.2092
V5: SeeSR+ Grounding + SRCA + STCFG Ungrounded Tags
26.3871
0.7625
0.2729
0,2095
V6: SecSR Grounding + -TCFG
26,3986
0.7627
0.2735
0.2112
V7: SeeSR Grounding + SRCA STCFG + Mask2Former
26.3128
0.7620
0.2725
0,2093
V8: SeeSR + Grounding + SRCA STCFG DINO-X
26.3449
0.7627
0.2722
0,2089
V9: SeeSR Grounding + SRCA+ Mask2Former
26,2885
0.7609
0.2734
0.2098
V10: SeeSR + Grounding SRCA + DINO-X
26.3221
0.7621
0.2729
0,2090

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augmenting the well-perfone performing DAPE tags using SS. However, as shown by comparing V4 and V7 in Tab. 2 performance declined, primarily because SS models, not trained with degradation in design. often produce inaccurate labels. Moreover, Mask2Former recognizes only 150 categories, limiting coverage for real-world SR tasks. This further reduce the semantic accuracy of the extracted texts.
To mitigate these constraints, we tried Prompt-Free Anything Detection and Segmentation in DINO- X [34] (V8 in Tab. 2). which supports open-vocabulary SS. Although it outperforms Mask2Former (V7), it still trails behind using only DAPE (V4), These results reaffirm that inaccurate tags prove more detrimental than incomplete ones an insight that also reinforces our grounding design.
4.4 Hyper-parameter analysis
The confidence threshold for grounding affects the balance between tag accuracy and coverage. A higher threshold discards less certain tags to boost accuracy, but also reduces coverage. As shown in Table 3 larger thresholds generally yield higher PSNR and SSIM, whereas thresholds of 0.25-0.35 achieve more favorable LPIPS and DISTS. Over-
Table 3: Hlpee-parameee analysis: different grounding thresholds produce consistent results that outperform the SeeSR baseline on RealSR dataset. Red = best, Blue second-best.
Threshold
PSNRT
SSIMT
LPIPS_
DISTS
Baseline
25.1717
0.7219
0,3008
0.2223
0.15
26.3632
0.7625
0.2727
0.2093
all, SRSR remains robust against varying thresh-
0.25
26.3996
0.7632
0.2718
0.2092
olds: in all cases, it significantly outperforms the baseline in all four metrics. We adopt a confidence threshold of 0.25 for all reported comparisons.
0.35
26.4550
0.7644
0.2715
0.2099
0.45
26.5378
0.7657
0.2731
0.2112
0.55
26.6008
0.7665
0,2735
0,2124
5 Conclusion
We introduced a spatially re-focused super-resolution (SRSR) framework to improve the semantic accuracy of cox-ioondiiooned, diffusion-bar -based SR. By grounding and filtering extracted tags, we remove irrelevant text prompts and pair each valid tag with corresponding segmentation mask. This enables spatially re-focused cross- Pross-atteniion (SRCA), which directs guidance to target regions, and spatially targeted classifier-fre guidance (STCFG). which preserves visual fidelity in ungrounded areas. Our approach operates entirely at inference time and can be applied to any cross-attention- based SR model that leverages text priors. Experiments on both synthetic and real-worle datasets show that SRSR outperforms seven -fate-of-the-art methods in fidelity and perceptual quality metrics.
6 Limitations and future works
While SRSR achieves strong gains in both fidelity and semantic accuracy, several limitations and future directions remain. First, further exploration of robust, degradation-awant segmentation models could reduce incomplete tag coverage and enhance tagging accuracy. Indeed, our ablation studies that compare Mask2Former with DINO-X suggest that stronger semantic segmentation (SS) backbones improve SRSR's performance, indicating that future innovations in SS can further enhance our method. Second, SRSR is currently designed as an inference-time framework to ensure Alug-and-ples compatibility with various pre-trained super-resolution models. Although this design enables broad applicability without retraining, incorporating SRSR into the training or fine-tuning process represents exciting future direction. Doing would allow the model to internalize the spatial and semantic constraints imposed by SRSR, potentially improving region- specific supervision and semantic localization. However, this integration would come at the cost of additional training complexity and resource demands. Third, SRSR's 's modular nature allows flexible integration with other Real-ISR frameworks. In this work, we demonstrated its generality through consistent improvements when applied to SeeSR (54) and OSEDiff (53] Future research could further validate this generality by extending SRSR to broader architectures such as AddSR [56] and TSD-SR [10]. Finally, existing no-reference quality metrics remain unreliable, as they often over-reward artifact-heavy or hallucinated outputs (see Fig. and Figures SST Hence, we emphasize full-reference fidelity and perceptual quality metrics as more trustworthy indicators of restoration faithfulness, which is critical in practical scenarios (e.g., e-commerce product imaging) where visual accuracy directly impacts trust and usability. Nevertheless, when high-quality references are unavailable, the dependence on current no-reference metrics becomes problematic. Developing hallucination-avv no-reference metrics that better align with full-reference measures and human perception an urgent research need. Promising directions include leveraging vasion-language models for perceptual assessment and incorporating penalties for semantic hallucinations.
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NeurIPS Paper Checklist
1. Claims
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Supplementary Material
Outline of supplementary material. This supplementary material provides additional results, analyses, and discussions to support the main paper. It is structured as follows:
In Section AA] we present extensive qualitative comparisons and visual analyses by comparing the baseline results and cross-attention maps before and after integrating our proposed SRSR framework. These examples demonstrate the effectiveness and internal mechanisms of the SRCA and STCFG components within the SRSR framework (Figures [Tigures/57($S1]
Section extends this analysis to an ablation variant that uses semantic segmentation masks for prompt extraction. This supplements the quantitative ablation study results in Section 4.3 of the main paper and supports the claim in Section 3.3 that semantic segmentation has two key limitations (lack of degradation awareness and constrained vocabulary size), which lead inferior results compared to our approach based on DAPE and Grounded SAM (Figures ST2 S15).
In Section C we provide additional qualitative comparisons paired with quantitative metrics to highlight key observation: no-reference metries often misjudge and favor hallucinated outputs (Figures(ST6
Section[Dpresente an additional user study evaluating human perceptual preferences between the baseline results and those produced after integrating SRSR (Figure [ST8). The study shows that human annotators consistently prefer our SRSR- ERSRR-enhanced outputs.
Section D supplements the ablation results in Tab with no-reference metrics (Tab. (S]) further showing that while full-reference fidelity and perceptual metries align with visual quality improvements, existing no-reference metrics often produce misleading trends and over-reward artifact-heavy outputs.
Finally, Section SectioonFirovideee provides clarification potential societal impacts.
All figure references include detailed captions to support discussion and analysis of the corresponding
findings.
A Additional qualitative comparisons and visual analysis
As a plug-and-play module, the effectiveness of SRSR most clearly demonstrated by comparing the performance of the same baseline before and after integration. In this section, we present additional qualitative comparisons and visual analyses (Figures (S to support the following key takeaways:
1. Enhanced semantic fidelity. We provide qualitative comparisons that highlight the ability of SRSR to improve semantic fidelity by removing hallucinations. While baseline results may appear visually appeuling, they often contain hallucinated objects or textures that are semantically inconsistent with the ground-truth high-resolun resoluleolllo image. In contrast, our method produces restorations that better align with the true image content.
2. Limitations of no-reference metrics. Alongside visual results, we include corresponding quantitative comparisons. We emphasize that no-reference metrics, which lack access to the ground-truth high-resolution image, tend to misjudge and often reward hallucinated outputs. This is evident in the performance drop of these metrics when hallucinations are removed. Conversely, full-reference metrics (PSNR and SSIM for fidelity, and LPIPS and DISTS for perceptual quality) provide more reliable assessment of semantic and visual accuracy. SRSR demonstrates superior performance in these full-reference evaluations.
3. Visual analysis SRCA and STCFG effects. To further illustrate the mechanisms of our proposed components, we include auxiliary visualizations such as the re-focused versus original cross-attention maps or the ungrounded region masks. These demonstrate how SRCA enhances text-token alignment with relevant regions and how STCFG effectively suppresses text influence in ungrounded areas to prevent hallucinations.
All figures presented in this section are accompanied by detailed captions to support discussion and analysis of the corresponding findings.
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B Comparing prompt extraction methods: DAPE vs. Semantic Segmentation
As discussed in Section 3.3 of the main paper, we also experimented with using semantic segmentation to extract grounded fag-region pairs, and compared its performance with our final method that employs DAPE and Grounded SAM for the same goal. We observed two key limitations when using semantic segmentation compared DAPE: First, unlike DAPE, which is trained to be degradation- aware when extracting tags from low-quality LR images, standard semantic segmentation models lack this capability and tend to extract incorrect tags more frequently, leading to restoring hallucinated contents that are not faithful to the ground-truth HR image. Second, Semantic segmentation models often have limited vocabulary size, which restricts their expressiveness and precision practice. This leads to the extraction of coarse prompts that result in imprecise or incorrect restorations. These issues result in inferior performance compared to our final approach, which uses DAPE for prompt extraction followed by Grounded SAM for visual grounding.
As reported in Section 4.3 of the main paper, we already presented ablation studies with quantitative comparisons. In this section, we supplement those results with additional qualitative comparisons and analysis. Specifically, the figures correspond to supporting the following key takeaways:
1. Semantic segmentation models lack degradation awareness. Figures S12 S13 highlight that this limitation causes incorrect prompts to confidenlyy extracted, which then misguide the restoration process and lead to semantically incorrect outputs.
2. Semantic segmentation models have constrained vocabulary size. Figures $14 STS
illustrate that this limitation reduces the expressiveness and precision of prompt extraction. As result, using only coarsely related prompts to guide the restoration process leads to suboptimal outcomes.
3. Limitations of no-reference metries. Same as in Section A all figures in this section (Fig- ures S13) further demonstrate the drawbacks of no-reference metries: they frequently misjudge and favor outputs that contain hallucinations resulting from incorrect text condi- tioning, demonstrating their limitations in assessing semantic fidelity for super-resolution.
All figures presented in this section are also accompanied by detailed captions to support discussion and analysis of the corresponding findings.
C More qualitative comparisons illustrating no-reference metrics' limitations
The previous two sections have already highlighted the limitations of no-reference metrics by com- paring our method against both the baseline and ablation variant that uses semantic segmentation masks for prompt extraction. In this section (Figures S17) we provide additional visual com- parisons (paired with corresponding quantitative metries) between our method and other baseline approaches to further underscore the shortcomings of no-reference metrics.
D Additional user study results
We have also conducted user study to evaluate human perceptual preferences between the bascline results before and after integrating our SRSR framework. Given the size of the synthetic DIV2K dataset, we focus the study on two real-worid datasets: RealSR and DReaISR, using their entire datasets. For each image, we presented the low-resolution input along with two anonymized high- resolution outputs (ours: SRSR-SeeSK and the baseline: SeeSR) labeled 'Method A' and 'Method B'. Two annotators were asked to select the preferred image or indicate if both appeared visually similar, with evaluations based on sharpness, visual realism, and detail preservation. The order of presentation was randomized, and full images were shown (not crops) to avoid bias. Results are aggregated and visualized in bar charts (Figure (T8) showing that SeeSR is consistently preferred over the baseline SecSR across both datascts.
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Table S1: Ablation study evaluating the contribution of each component within SRSR. SRCA denotes spatially re- re-focused cross-attention, STCFG denotes spatially targeted classifier-free guidance, and G refers grounding. Mask2Former and DINO-X represent different semantic segmentation backbones. Bold indicates the best performance.
Versio
PSNKT
SSIMET
LPIPSL
DISTS:
F1D
NIQEE]
MUSIQT
MANIQAT
CLIPIQAT
V1: SecSR
25.1717
0.7219
0.3008
0.2225
125.55
5.4081
69.77
0.6442
0,6612
SecSR
V3: SocSR + +RRRCC
0,7280
0.3013
0.2254
132.55
5.5029
69.91
0.6422
V4: SecSR + SRCA STCRG (Chrs)
26.399
0.7632
0.2718
0.2092
126.5 31
5.8627
62.88
0.5628
0.5407
V5: V4 Uiggnunded Tags
36 3838771
0.7625
0.2729
0.2095
126.28
5,8657
63.15
0.5666
0.5460
V6: SceSR G STCFG
26,3986
0,7627
0.2735
127.24
5,8947
62.99
0.5648
0.5456
SecSR
SRCA STCRG Mask2Fommer
26.3128
127.51
V8: SoeSR SRCA STCRG
127.77
5,899
19: SeeSR SRCA Mack2Farmer
26 2835
0,7009
0.2734
0.2098
127.19
5.7801
63.73
0.5728
0,5543
V10: SeeSR G SRCA DINO.X
26.3221
0.7621
0.2729
0.2090
127.33
5.7927
63.58
0.5713
0,5520
E Extended ablation results
To further strengthen our point that the existing no-reference metrics are flawed that they tend to over-reward artifact-heavy or hallucinated outputs, in Tab. ST supplement the no-reference metrics to the ablation results in Tab. 2
Tab. ST highlights a consistent trend: improvements in full-reference fidelity metrics (PSNR, SSIM) and perceptual quality metrics (LPIPS, DISTS) align with observed improvements visual and semantic fidelity, as supported by our qualitative analysis (c.g., Fig. 4add Figures S11) However, the same changes often lead worse results in all no-reference metrics (NIQE, MUSIQ. MANIQA. CLIPIQA), which can misleadingly favor outputs with hallucinated or artifact-heavy details. For example, removing STCFG (V3) from our full version (V4) improves all no-reference metries, even though fidelity, full-reference perceptual quality, and visual quality drop. Similarly, removing SRCA (V6) worsens both fidelity and full-reference perceptual quality, yet again, no-reference metrics do not consistently reflect this decline. This pattern also holds when comparing DINO-X to Mask2Former (V8/V7, V V10/V9): fidelity, full-reference perceptual quality metrics, and visual quality are better with elaborated
DINO-X, which aligns with the inherent advantages of DINO-X over Fosk2Former, in Sec. [4.3] However, no-reference metrics show the reverse trend. These findings reinforce our point that current no-reference metries often fail to penalize hallucinations and can mislead practical evaluation, especially in Real-ISR.
Clarification on potential negative societal impact
Our work focuses on enhancing the visual quality of low-resolution images through super-resolution techniques with improved semantic fidelity. It is primarily intended for applications in image restoration, photography, and scientific imaging, where enhancing degraded real-world content valuable. Our method does not involve any user or personally identifiable information, and our evaluation is conducted on public, non-sensitive datasets. Therefore, we believe our work does not pose significant societal risks. On the contrary, it may benefit the aforementioned intended applications where detail preeellireervinng enhancement is crucial.
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Ground-truih HR image
SeeSR result w/ hallucinaaions
"animal"
"stope'
Full-reference
PSNR:
SSIM:
LPIES: 0.3500
DISTS: 0.2866
No-reference
NIQE: 3.02
75.19
MANIQA: 0.6647
CLIPIQA: 0,8222
Zanomed LR image
Ours result w/o hallucinations
"animal'
'stone'
Full-reference
SSIM: 0.5519
LPIPS: 0.3456
No-reference
MUSIQ
MANIQA: 054509
CLIPIQA:
Figure S1: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Attention visualizations for selected tokens illustrate the effect of SRSR. In the region highlighted by the red bounding box, the object is actually an animal's claw, but it is difficult to recognize the degraded LR image. Consequently, the prompt extractor (DAPE) fails to extract relevant tags such as 'claw', As result, the baseline model attributes this region to the token 'animal' according to the cross-attention map, and hallucinates it as a vivid fish. Similarly, in the yellow bounding box, the region corresponds to the animal's fur, but is misinterpreted due to degradation. The baseline instead applies influence from the unrelated tag 'stone', resulting in texture resembling small pebbles. Beyond these highlighted objects, the baseline also introduces other over-syntheszee textures in the 'animal' and 'stone' regions that deviate from the ground-truth -ruth HR image, despite being visually plausible. In contrast, our SRSR framework assigns tags only to regions where grounding confidence high (e.g., 'animal', 'stone"), leaving uncertain regions, such as the red region, ungrounded. Within the SRSR framework, SRCA ensures that grounded regions are not influenced by irrelevant tokens, thereby removing hallucinations from the 'animal' and 'stone' areas. Additionally, STCFG applies unconditional predictions to ungrounded regions like the animal's l's claw, suppressing inappropriate text influence while preserving perceptual quality.
Ground-truih image
SocSR hallucinations
'camoullage'
'gun"
Full-reference PSNR: 30.22
SSIM: 0,5720
LPIPS:
0.1519
Novreferecce NIQE: 4.35
MUSKQ: 57.74
MANIQA: 0.5277
CLIPIAA 0.5306
Zooomed image
Ours hallucinations
'camoullage'
"soldier"
Full-effrrence
PSNR: 33.40
SSIM:
LPIPS:
DISTS: 0.1290
No-reference
NIQE: $.20
MUSIQ: 53.18
MANIQA:0.000
CLIPIQA: 0.4484
Figure S2: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Attention visualizations for selected tokens to illustrate the effect of SRSR. In the highlighted region, the baseline hallucinates gun-like objects due to incorrect tags like 'gun' and 'rifle' extracted by DAPE, which misattribute attention to parts of the 'camouflage' object. In contrast, our SRSR framework removes such irrelevant tags via grounding and re-focuses attention on salient concepts like "camoullage" and 'soldier', resulting in semantically faithful restorations.
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Ground-truth HR image
SceSR result w/ hallucinations
'nose'
Taco'
Full-roference
SSIM:
LPPPP:: 0.4874
DISTS, 0.2936
No-reference
NIQE: 6.19
MUSKQ;
MANIQA: 0.4744
CLIPIQA: 0,6775
Zooomed LR image
Our result w/o hallucinations
Ungrounded mask
'face'
Full-reference
SSIM: 0.8744 LPIPS: 0.4585
DISTS:0.261
No-reference
MUSIQ
MANIQA:
CLIPIOA:
Figure S3: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. In the highlighted region, the baseline hallucinates nose hairs on the face due to incorrect tags like 'nose' and 'mouth' extracted by DAPE, which misattribute atfention to parts of the 'face' object. In contrast, our SRSR framework removes such irrelevant tags via grounding and re-focuses attention on salient concepts like 'face', resulting in semantically faithful restorations.
Ground-truth image
SocSR result w/ hallucinations
'bair'
'stand'
PSNK: 23.10
SSIM: 0.6147
0.4155
NIQE:
MUSIQ 68.89
MANIQA: 0.6237
CLIPIQA: 0,5756
Zooomed LR image
Ours hallucinations
Ungrounded
'swcater"
Full-referecce
PSNK: 26.03 SSIM:
LPIPS:
DISTS: 0.2015
No-reference
NIQF: 4.66
MUSIQ: 58.35
MANIQA:
CLIPIQA: 0.4541
Figure S4: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. In the highlighted region, the baseline produces sweater textures that deviate from the ground-truth HR image, influenced by unrelated tags such as 'hair' and incorrect tags like 'stand', as indicated by the cross-attention maps. In contrast, SRSR framework employs SRCA eliminate irrelevant tag influence and localize text conditioning strictly to grounded regions, associating the sweater region only with the tag 'sweater', thereby ensuring more faithful and semantically accurate restorations.
27

---

Ground-truth HR image
SecSR result w/ hallucinations
Ungrounded mask
"animal
Full-reference
PSNN: 23,15
SSIM:
1PHIP::
DISTS: 0.2567
No-reference
NIQE: 5.16
MUSIQ 52.75
MANIQA:
CLIPIQA: 0,8202
image
hallucinations
Ungrounded
rufleneeerence PSNR
LPIPS: 0.5567
DISTS.0.20 0.2662
No-reference
NIQE: 5.38
MANIQA:
CLIPIQA: 0,4503
Figure S5: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. In the baseline, the inherent cross-attention for the token 'animal' is broadly dispersed across the image, including the highlighted region corresponding to the stone object, leading to hallucinated animal-like features that area (note the stone is mistakenly restored with fur-like textures). Our SRSR framework addresses this by first applying SRCA to constrain the token 'animal' to its grounded region, correcting misaligned attention. However, ungrounded regions remain susceptible to influence from global tokens such EOS, padding, and punctuation, which can introduce noise and semantic summary of the entire prompt. To address this, we introduce STCFG to explicitly avoid applying text conditioning to ungrounded regions, resulting in more semantically faithful restorations.
Ground-truth image
SocSR result w/ hallucinations
'lay'
Full-reference PSNR: 22.47
SSIM:
DISIS: 0.2867
Ne-reference
NIQE: 5.96
MUSIQ: 57.32
0451
CLIPIQA: 0.7582
Zooomed image
Ours result hallucinations
Ungrounded mask
'lay"
Full-reference
PSNK:
LPIPS: 0.6910
01295:002922
No-reference
NIQF: 8:00
MUSIQ
MANIQA: 0.2666
CLIPIQA: 0.3240
Figure S6: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. The highlighted region heavily degraded and should depict stone and weed in the ground- truth HR image. However, the baseline incorrectly restores it as cat's face with whiskers, which is visually plausible semantically inaccurate. The baseline's cross-attention maps reveal that punctuation token and the irrelevant token 'lay" attend to this region. These lack meaningful semantics or relevance to the scene, resulting in hallucinated content during restoration. By contrast, SRSR framework applics SRCA to constrain the influence of tokens like 'lay" their grounded regions and uses STCFG to apply unconditional generation in ungrounded areas (as identified by the ungrounded mask), effectively suppressing hallucinations and improving semantic fidelity.
28

---

Ground-truth image
SeeSR result w/ hallucinations
'table"
stool
PSNK: 31.65
SSIM: 0.53400
LPIPS: 0,3690
0.2401
No-reference
NIQE:
MUSIQ: 73.22
MANIQA:
CLIPIQA: 0.6748
Zovomed LR
Ours result w/o hallucinations
Ungroumded mask
"stool'
Full-rderence
PSNR: 33.89
SSIM: 0.8987
0,3435
0.2186
No-reference
NIQE: 8:03
MUSKQ 59.53
0411
CLIPIDA 0.5133
Figure S7: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. The key objects in this example two stacks of plates, each placed While DAPE successfully extracts the semantically related tag "stool' for the base structures, it fails recognize the plates due severe degradation in the LR image. In the baseline, the cross-attention for the tag 'stool' diffusely distributed across irrelevant regions, and additional tags such as 'table' and 'stool" incorrectly attend the plate areas. This misattribution results in visible artifacts in the restored plates and suboptimal reconstruction of the base. In contrast, our SRSR framework leverages SRCA re-focus attention on correctly grounded regions (e.g., the base) and uses STCFG to suppress text-based influence in ungrounded regions (e.g., the plates), yielding more accurate and faithful restorations.
supporting base.
Ground truth HR image
SeeSR result w/ hallucinations
'waler'
'stone'
Full-reference
SSIM: 0.3538
LPIPS: 0.3570
No-reference
MUSIQ
MANIQA: 0.6652
0.7501
Zaoamed L.R image
Ours result w/o hallucinations
"water"
'building"
PSNR: 19.27
SSIM: 0.3563
LPIPS: 0.3474 DISTS:O
No-reference
NIQE::
MUSIQ: 73,90
MANIQA: 0.6812
CLJPIQA: 0.7236
Figure S8: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Attention visualizations for selected tokens to illustrate the effect of SRSR. In the highlighted water regions, the baseline introduces unnatural patterns, influenced by the irrelevant token 'stone" and the misdirected attention of the relevant token 'water' to incorrect spatial areas. By contrast, our SRSR framework leverages SRCA to re- re-focus each token's 's influence within its grounded spatial region, producing semantically more faithful and visually accurate restorations.
29

---

Ground-truth HR image
SecSR result w/ ballucinations
Full-reeeence
SSIM: 0.8720
LPIP5:0.1991
0.1519
No-reference MUSIQ:
MANIQA 0.5277
Zooomed I.R image
Ours result w/o bullucinations
PSNR:
SSIME 0.9252
LPIFS: 0.1457
No-reference NIQE:
MUSIQ: $3.18
MANIQA: 0.4132
CLIPIQA:
Figure $9: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. In this example, DAPE fails to extract any meaningful tags, leaving only global tokens (e.g., SOS, EOS, punctuation) to drive text conditioning. The baseline forces the application text guidance even in such cases, causing the model to hallucinate textures despite the prompt carrying semantic value. In contrast, our SRSR framework uses STCFG to disable text conditioning in ungrounded regions (here, the entire image), applying only unconditional prediction during denoising. This suppresses hallucinated details and improves alignment with the ground-truth HR image.
Ground-truth HR image
SccSR result w/ hallucinations
Full-reerence
PS.1:
SSIM:
LPIES: 0U6271
DISES: 0.3653
No-reference
MUSIQ:
MANIOA: 0.5635
CLIPIOA:
Zonomed LR image
Ours result w/o bulluciaations
PSNR: 34.61
SSIME: 0.8200
LPIPS-005744
No-reference NIQE:
MUSIQ: 45.51
MANIQA: 0.5420
CLIPIA:
Figure S10: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. In this example, DAPE fails to extract any meaningful tags, leaving only global tokens (e.g., SOS, EOS, punctuation) to drive text conditioning. The baseline forces the application text guidance even in such cases, causing the model to hallucinate textures despite the prompt carrying semantic value. In contrast, our SRSR framework uses STCFG to disable text conditioning in ungrounded regions (here, the entire image), applying only unconditional prediction during denoising. This suppresses hallucinated details and improves alignment with the ground-truth HR image.
30

---

Ground imuge
SeeSR hallucinations
Ungroumded
Full-reeeeence
PSNR: 30.56
SSIM: 0,9041
DISTS: 0.2799
No-reference
NIQU: 11.08
MUSKQ: 60,02
MANIJA:
CLIPIQA: 0.5994
image
hallucinations
Ungroumded
"sky
Full-rdference
PSNR: 39,25
LPIPS: 0.0772
DISTS: 0.1584
No-reference
NIQE: 12.87
MUSKQ:
MANIQA:
CLIPIQA: 0,3619
Figure S11: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons of the baseline with and without our proposed SRSR. Column 3: Corresponding quantitative metrics. Columns 4-5: Ungrounded region mask and attention visualizations for selected tokens to illustrate the effect of SRSR. In the baseline, the inherent cross-attention maps show that only the punctuation token attends to the highlighted region. However, this token carries no meaningful semantics, and its influence during the generation process introduces noise and hallucinated details that deviate from the ground-truth HR image. In contrast, our SRSR framework leverages STCFG to apply unconditional generation specifically to regions that cannot confidently grounded (i.e., those indicated by the ungrounded mask), effectively removing such hallucinations and improving semantic fidelity.
Ground-truth HR image
Ours result w/ Semantic Segmentation
'earth, ground"
'tree'
Full-efference PSNR:
SSIM: 014006
LPIPS: 0.2866
No-reference
MUSIQ 73.05
MANIGA:0.800
CLIPIQA:
Zovomed LR image
Ours result w/ DAPE Grounded SAM
Ungrounded musk
'tree'
PSNR: 22.06
SSIM: 0.5063
NIQE: 3,80
MUSIQ: 71.73
MANIQA: 0.6895
CLIPIDA 0.8045
Figure S12: Column I: Paired LR-HR images. Column 2: Qualitative comparisons our results using semantic segmentation versus DAPE Grounded SAM for prompt extraction and grounding. Column 3: Corresponding quantitative metrics. Columns 4-5: Grounded masks for selected tokens, where white indicates grounded regions. Unlike DAPE, which is trained to be degradation-awan when extracting tags from low-quality LR images, standard semantic segmentation models lack this capability and tend to extract incorrect tags more frequently. In the highlighted region, the semantic segmentation model confidently grounds the tag 'earth, ground", leading to a ground-like in the HR image. In contrast, DAPE
restoration that is inconsistent with the true content (trees) more degradation-awarc thus fails to confidently ground any tag for this region due to the severe degradation, triggering STCFG mechanism to apply unconditional generation. This results in more faithful tree-like restoration and effectively prevents the introduction of semantically incorrect content.
31

---

Ground-truih image
Ours Semantic Segmentation
"tray"
"sink'
Full-rdfereace
PSNR: 32.19
SSIM: 0.582
NIQE:
MUSIQ 71.34
MANIQA: 0.000::0600555
CLIPIQA 0.6008
Zooomed image
w/ DAPE Grounded SAM
Ungrounded
'stool'
Full-refereace
PSNR: 33,89
SSIM:
1PIPS:
DISTS: 0.2186
No-roference NIQE: 8.03
MUSIQ 59.53
0551QA:05514
CLIPIQA: 0,5133
Figure S13: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons our results using semantic segmentation versus DAPE Grounded SAM for prompt extraction and grounding. Column 3: Corresponding quantitative metrics. Columns 4-5: Grounded masks for selected tokens, where white indicates grounded regions. Unlike DAPE, which is trained to be degradation-aware when extracting tags from low-quality LR images, standard semantic segmentation models lack this capability and tend extract incorrect tags more frequently. semantic segmentation model grounds tags such as 'tray' and 'sink', which leads to hallucinated content misrepresenting both the plates and the base beneath them. contrast, DAPE extracts the tag 'stool", which more appropriately describes the supporting base. For the severely degraded plate regions, DAPE does not assign any confident tag, triggering our STCFG mechanism apply unconditional generation. This helps produce more semantically accurate restorations and avoids introducing misleading visual artifacts.
the highlighted region, the
Ground- image
Semantic Segmentation
'person"
Full-reference PSNR: 24.28
SSIM: 0.6481
LPIPS:
NIQB: 4.11
MUSIQ: 68.04
MANIQA::: 0.5834
0.5834
CLIPIDA 0.5548
Zooomed LR image
Ours result w/ DAPE Grounded SAM
Ungrounded mask
'swcater
Full-reference PSNR: 26.03 SSIM:
LPIPS:
DISTS: 0.2015
No-roference NIGE: 4.66
MUSIQ: 58.55
MANIQA:0.500
CLIPIQA: 0.4541
Figure S14: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons our results using semantic segmentation versus DAPE Grounded SAM for prompt extraction and grounding. Column 3: Corresponding quantitative metrics. Columns 4-5: Grounded masks for selected tokens, where white indicates grounded regions. Due the limited vocabulary of semantic segmentation models, the extracted prompt ("person') is a coarse descriptor and leads to imprecise restorations of the sweater's textures. In contrast, our method leverages DAPE with a broader vocabulary and grounds the more appropriate tag 'sweater' via Grounded SAM, resulting in semantically faithful restorations.
32

---

Ground-truth HR image
Ours result w/ Semantic Segmentation
mmountain, mount
Full-eeference PSNR: 22.33
SSIM: 0.5105
LPIPS:
0.3350
NIQE: 3.67
MUSIQ: 72.21
MANIQA: 0.6357
CLIPIQA: 0.8175
Zooomed image
DAPE Grounded SAM
'rock Face"
Full-eeferwnee 24.53
SSIM:
LPIPS: 0,3223
DISTS: 0.1966
No-reference
NIQE: 5.15
MUSIQ:
MANIQA: 0.5188
CLIPIQA: 0.7596
Figure S15: Column 1: Paired LR-HR images. Column 2: Qualitative comparisons our results using semantic segmentation Column 3: Corresponding quantitative metrics. Columns 4: Grounded masks for selected tokens, where white indicates grounded regions. Both approaches ground the entire image with single tag, as reflected by the fully white masks. However, due to the limited vocabulary of semantic segmentation models, the extracted prompt ('mountain, mount') ') coarse descriptor and leads to mountain-lin like hallucinations. In contrast, our method leverages DAPE with a broader vocabulary and grounds the more appropriate tag 'rock face' via Grounded SAM, resulting semantically faithful restorations.
DAPE Grounded SAM for prompt extraction and grounding.
33

---

Ground-truhh HR image
Baselines result ballucinations
Ours result w/o hallucinations
Full-reforenec
Full-eeference
27.33
SSIM 0.932
SSIM: 0.96
1 PIPS 0.1074 DISTS:
LPIPS, 0.1037
0.1722
NIQE: 10.64
NIQE: 12.15
MUSIQ: 64.09
MUSIQ2 54,86
MANIQA: 0,5370
MANIQA: 0.4626
CLIPIOA: 0.7957
CLIPIQA: 0.6521
Full-reference
Full-rdeeeecce
PSNR: 26.05
PSNK: 26.23
0.8097
SSIM: 0.8305
LPIPS: 0.2267
No-rderencee
No-reference
NIQE: 5.27
NIQE: 7.43
MUSIQ: 74.49
MUSIQ 73.51
MANIQA:
CLIPIQA:
CLIPIQA: 0,7485
Full-refeeenee PSNR: 23.15
Full-reference
PSNK: 24.88 SSIM:
SSIM:
LPM55:0.1885
DISTS: 0.19K3
DISTS: 0.1897
No-refereace
No-reference NIQE: 5.02 55.41
NIQE: 3,10 MUSIQ:
MANIQA:
MANIQA:
CI CLIPIQA:
CLIPIQA: 0.6180
Full-reference PSNR: 23.91
Full-reference
PSNR: 27.24
LPM95-03994
LPIPS: 0.2857
DISTS: 0.2518
DISTS: 0.1530
Ne-reference
No-seference NIQE:
NIQE: 4,31
MUSIQ
MANIQA: 0.5041
MANIQA: 0.5726
CLPPIQA-..818
CLIPIQA: 0,5750
Full-reference SSIM 0M45-0
Full-eeference
LPIPS: 0,2957
DISTS: 0.2321
DISTS: 0.1565
No-reerencc
No-reference
NIQE:
NIQE: MUSIQ 66.15
MUSIQ: 72.02
MANIQA: 0.6062
MANKQA: 0.4729
CLIPIQA:0300
CLIPIQA: 0.7932
Figure S16: Additional qualitative results paired with quantitative metrics reveal that no-reference metrics tend to misjudge and heavily reward hallucinated results. This exposes their limitations in assessing semantic fidelity super-resolution tasks.
34

---

Ground-truhh HR image
Baselines result ballucinations
Ours result w/o hallucinations
Full-reforenee
Full-eeference
SSIM: 0. 5301
SSIM: 0.40K5
1 LPIPS: 0L5507 DISTS:
LPIPS: D.4488
DISTS:00216
NIQE: 3.94
NIQU:
MUSIQ:52.1M 52.18
MUSIQ: $8.97
MANIQA: 0.5951
MANIQA: 0.5212
CLIPIQA: 0.6970
CLIPIQA: 0.4137
Full-reference
Full-reference
PSNR: 24.95
PSNK: 25.78
0.0993
SSIM: 0.7436
LPIPS:
0.1116
No-rderencce NIQE: 5.33
No-reference
NIQE: 6.37
MUSIQ: 58.68
MUSIQ 42.89
MANIQA-0.5179
MANIQA:
CLIPIQA:07575
CLIPIQA: 0.4323
Full-referenee PSNK 28.17
Full-reference
PSNK: 51.84
SSIM:
SSIM:
1.215:0021.6
DISTS:
DISIS: 0.1370
No-reference NIQE: 5.93 MUSIQ:
No-reference NIQE: 6.22
MANIQA:
MANIQA:
CLJPIQA: 0,5731
CLIPIQA: 0.4901
Full-reference PSNR: 23.64
Full-reference
PSNR: 32.80
LPIP:-09110
LPIPS: DISTS: 0.3909
DISTS: 0.3978
Ne-reference NIQE: 3.50
No-seference NIQE:
MUSIQ: 65.77
MUSIQ
MANIQA: 0.6918
MANIQA: 0.2965
CLIPQAA-6112
CLIIPIQA: 0.2303
Full-reference
Full-eeference PSNR:
21.58
LMPS- 0.193
LPIPS: 0,4152
DISTS: 0,2100
DISTS: 0919
No-reerencce
No-reference
NIQE:
NIQE: MUSIQ: $7.73
MUSIQ: 70.02
MANIQA: 0.6558
MANIQA: 0U6182
CLIPIQA:0.00
CLIPIQA:
Figure S17: Additional qualitative results paired with quantitative metrics reveal that no-reference metrics tend to misjudge and heavily reward hallucinated results. This exposes their limitations in assessing semantic fidelity super-resolution tasks.
35

---

Dread8R
RealSR Test Set
100
Percentage of Votes by Annotators(())
337.4%
41.0%
34.0%
27.3%
35.0%
Hoseline
Simila
Haseline
Similar
Figure S18: User study results evaluating human perceptual preferences between the baseline results and those produced after integrating SRSR. The study shows that human annotators consistently prefer our SkSR-enbanced outputs.
36


Reviews:
[{"review_id": "yg2oFcIUf2", "reviewer": "Reviewer_RFp9", "summary": "This paper presents SRSR, a plug-and-play and training-free framework that can be easily applied to Stable-Diffusion-based SR methods with text conditions. The proposed method enhances semantic fidelity by grounding tags to guide cross-attention and suppress irrelevant text influences on ungrounded regions. SRSR improves both visual quality and alignment with semantics and also alleviates hallucination, outperforming baselines across multiple datasets and full-reference evaluation metrics.", "questions": "1. In Table 2 V5, can you explain how you add Ungrounded Tags? Do these ungrounded tags also associate with segmentation maps? Otherwise, how to use these ungrounded tags with SRCA and STCFG?\n2. Do you try to apply the proposed method to other recent works like AddSR and TSD-SR?\n3. It appears that the proposed method offers greater improvement to the multiple-step method, such as SeeSR, compared to the single-step method like OSEDiff. Have you conducted experiments to evaluate how the diffusion steps affect the results with the proposed method?", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "# Strengths\n+ The proposed method is simple, straightforward, and intuitive.\n+ The experiment results demonstrate that the proposed method can be easily plugged into existing text-conditioned Stable-Diffusion-based SR methods and improve the SR quality with minimal additional burden.\n+ Overall, the writing and figures are clear and enjoyable to read.\n# Weaknesses\n+ This paper argues that previous methods using a segmentation model like Mask2Former and Prompt-Free Anything Detection and Segmentation in DINO-X lack degradation-awareness and thus lead to inferior results. However, the Grounded SAM they used is not degradation-aware, either.\n+ Grounding shows only slight improvements compared to the baseline, as seen in V1 and V2 of Table 2. This could weaken the argument that the accuracy-completeness trade-off is important and that accuracy typically outweighs completeness.", "was_revised": true, "final_justification": "The authors have provided clear and thorough responses that answered my question and addressed my concerns. While the reliance on CFG may limit the applicability of SRSR to some extent, the method remains a valuable and effective plug-in for compatible diffusion SR models. I have updated my score to reflect a more positive assessment of the paper.", "rebuttal": "We sincerely thank the reviewer for their thoughtful evaluation and positive assessment of our work regarding several important aspects:\n\n- **Comprehensive experiments and effective results**: “SRSR improves both visual quality and alignment with semantics and also alleviates hallucination, outperforming baselines across multiple datasets and full-reference evaluation metrics,”\n- **Practical merits**: “The experiment results demonstrate that the proposed method can be easily plugged into existing text-conditioned Stable-Diffusion-based SR methods and improve the SR quality with minimal additional burden,” and “a plug-and-play and training-free framework that can be easily applied to Stable-Diffusion-based SR methods with text conditions.”\n- **Intuitive and original methodology**: Recognized by the numerical rating (“Originality: 3: good”) and the encouraging comments: “The proposed method is simple, straightforward, and intuitive,” and “the proposed method **enhances semantic fidelity** by grounding tags to guide cross-attention and suppress irrelevant text influences on ungrounded regions.”\n- **Clarity and presentation**: “Overall, the writing and figures are clear and enjoyable to read.”\n\nFinally, we appreciate the reviewer’s constructive feedback in the weaknesses and questions sections, and we have addressed each point thoroughly in the responses below.\n\n## W1: Why is our framework degradation-aware?\n\nThanks for this important concern regarding degradation-awareness. We would like to clarify below the fundamental difference between our proposed framework and the alternative semantic segmentation-based designs (Mask2Former and Prompt-Free Anything Detection and Segmentation in DINO-X), highlighting the key reasons for our superior results.\n\nWhile we acknowledge that Grounded SAM itself is not inherently degradation-aware, **our framework introduces degradation-awareness explicitly at the prompt extraction stage (prior to employing Grounded SAM)**. Specifically, we utilize a Degradation-Aware Prompt Extractor (DAPE), which is carefully fine-tuned from the Recognize Anything Model (RAM) to ensure extracted text tags are robust and accurate even under degraded, low-resolution (LR) conditions. Consequently, when these degradation-aware tags from DAPE are provided as inputs to Grounded SAM, the resulting prompt–mask pairs inherently exhibit enhanced degradation-awareness and accuracy. In contrast, semantic segmentation methods directly assign segmentation labels as prompts without a separate degradation-aware prompt extraction step, thus limiting their robustness and accuracy for degraded LR images.\n\nTo substantiate this claim, we also conducted comprehensive analyses:\n\n- **Quantitative ablation studies (Sec. 4.3, Table 2):** Variants V7 (Mask2Former) and V8 (DINO-X), both lacking degradation-awareness, show clearly inferior results compared to our proposed approach (V4).\n- **Qualitative analysis (Supplementary Sec. B, Figures S12–S15):** For example, Figures S12 and S13 demonstrate how the lack of degradation-awareness in semantic segmentation methods *causes incorrect yet confidently-extracted prompts*, subsequently misguiding restoration and producing semantically incorrect outputs. In contrast, our proposed DAPE + Grounded SAM framework effectively addresses this limitation, yielding substantially improved semantic correctness.\n\nWe will ensure these clarifications are further emphasized in the revised version.\n\n## W2: Grounding and Accuracy-Completeness Trade-off\n\nWe appreciate the reviewer’s thoughtful comment regarding grounding and the accuracy-completeness trade-off. Below, we unpack this issue into three parts to comprehensively address your concern:\n\n1. The importance of the accuracy-completeness trade-off;\n2. Why accuracy typically outweighs completeness;\n3. The broader contribution of grounding beyond direct performance improvement.\n\n### (1) Importance of accuracy-completeness trade-off:\n\nAccuracy and completeness of the extracted prompts are two of the three key research gaps we explicitly identified in Section 3.1. In our preliminary exploration (Table 2, comparing V1 to V2), we found grounding to be a promising direction as it can provide slight improvements to the baseline by simply applying grounding to improve on accuracy at the cost of completeness (without integrating deliberately designed modules such as SRCA and STCFG). At this stage, we also observed that grounding alone yields only small improvements, as the accuracy gains come at the cost of completeness, resulting in modest net gains. This clearly highlighted the necessity of explicitly addressing the accuracy-completeness trade-off. This motivated our design of the STCFG module, which significantly improved this trade-off and overall restoration.\n\n### (2) Why accuracy typically outweighs completeness:\n\nThe importance of accuracy over completeness is much more significant after employing STCFG to address the aforementioned important challenge of the accuracy-completeness trade-off. Our core insight is that reduced completeness mainly affects ungrounded regions. STCFG targets these specifically, reducing issues from incomplete coverage and **making the completeness a much less concerning issue than incorrectness**.\n\nAfter integrating STCFG (V6), we observed substantial gains compared to V1, showing that optimized accuracy outweighs losses in completeness. Further evidence supporting this claim comes from comparing our DAPE + Grounded SAM approach (V4) against segmentation-based methods (V7 and V8). Although segmentation methods inherently achieve higher completeness, their less accurate (due to poorer degradation-awareness) prompts directly led to inferior overall performance relative to our method.\n\n### (3) Contribution of grounding beyond direct performance improvement:\n\nFinally, we emphasize that grounding's contribution extends significantly beyond the performance improvements from V1 to V2. Crucially, grounding provides the essential tag–mask pairs necessary for enabling our SRCA and STCFG modules, without which these key innovations would not be feasible.\n\n## Q1: Implementational detail\n\nWe appreciate the opportunity to clarify the implementation of the ablated version V5. V5 first retains the entire settings of V4 and builds on it by additionally including ungrounded tags to demonstrate their negative impact. Specifically, V5 retains the SRCA refinement for grounded regions, where tag–mask pairs are available, and continues to use STCFG to block text influence in ungrounded regions. The only change is that, for V5, in addition to V4, we also keep the ungrounded tags and allow their default (inherent) cross-attention maps, **without applying SRCA**, since these tags do not have associated segmentation maps (as the reviewer correctly noted).\n\nBecause STCFG continues to restrict text influence in the ungrounded regions, the effect of these ungrounded tags is limited to their flawed cross-attention behavior within the union of all grounded regions. This setup highlights the drawback of including ungrounded tags: without the benefit of SRCA, their attention may be diffused or misdirected across the object regions, as seen for the “camouflage” tag in the bottom example of Fig. 2 that caused the baseline’s hallucinated object (highlighted in red), where the SRCA map would otherwise be entirely black and help effectively avoid the hallucination.\n\n## Q2: Application to other works\n\nThank you for this insightful question. We have not yet applied SRSR to AddSR or TSD-SR, but due to the plug-and-play nature of our framework, SRSR can be flexibly integrated into these and other recent approaches to further enhance their performance. In this work, we demonstrated the generality and effectiveness of SRSR by incorporating it into both SeeSR and OSEDiff, and we observed consistent improvements across multiple benchmarks and evaluation metrics. We will include a discussion in the revised manuscript (Future Work section) to highlight the potential for SRSR to benefit existing methods like AddSR, TSD-SR, and future methods. We believe the community will benefit from broader adoption of SRSR, and we encourage future research to explore its integration with a wider range of Real-ISR frameworks.\n\n## Q3: Different diffusion steps\n\nThank you for this valuable question. We would like to clarify that the greater benefit observed for SeeSR, compared to OSEDiff, primarily results from the use of STCFG rather than differences in the number of diffusion steps. Specifically, in OSEDiff, CFG is disabled by default, which renders STCFG inapplicable, meaning the performance gain comes solely from SRCA. In contrast, SeeSR benefits from both SRCA and STCFG, leading to more substantial improvements.\n\nMotivated by the reviewer’s insight, we conducted additional controlled experiments to further evaluate the effect of diffusion steps on performance, keeping all other factors fixed. For both our method and the baseline, we compared results at 50, 40, and 30 sampling steps. As shown in the table below, while reducing the number of diffusion steps introduces the typical fidelity–perceptual quality trade-off (i.e., fewer steps improve on fidelity metrics PSNR/SSIM but worsen perceptual quality metrics LPIPS/DISTS for both methods), our method consistently maintains a significant and stable advantage over the baseline across all metrics and step choices.\n\n| Version | PSNR↑ | SSIM↑ | LPIPS↓ | DISTS↓ |\n| --- | --- | --- | --- | --- |\n| Ours-s50 | **26.3996** | **0.7632** | **0.2718** | **0.2092** |\n| SeeSR-s50 | 25.1717 | 0.7219 | 0.3008 | 0.2223 |\n|  |  |  |  |  |\n| Ours-s40 | **26.5189** | **0.7644** | **0.2751** | **0.2122** |\n| SeeSR-s40 | 25.3804 | 0.7290 | 0.3027 | 0.2225 |\n|  |  |  |  |  |\n| Ours-s30 | **26.7346** | **0.7670** | **0.2777** | **0.2164** |\n| SeeSR-s30 | 25.5453 | 0.7316 | 0.3059 | 0.2272 |\n|  |  |  |  |  |"}, {"review_id": "mj6xpRT2zO", "reviewer": "Reviewer_pH61", "summary": "This paper proposes a plug-and-play spatially re-focused cross-attention based on the discovery that traditional cross-attention tends to divert towards irrelevant pixels. Combined visual grounding, the devised super-resolution framework can effectively avoid the interference of unrelated pixels.", "questions": "Please refer to the weakness.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**Strength**\n1. Visually, this work reveals the fact that the vanilla cross-attention induces hallucinations, while the devised spatially re-focused cross-attention can address this issue in some extent.\n2. The fidelity of super-resolved images are significantly improved.\n\n**Weakness**\n1. The design of the proposed modules are not novel, which are guided by the segmentation masks.\n2. The writing should be further improved. For instance, the abstract is too short and the first paragraph is too long.\n3. In Figure 1 (left), the body of bird is not highlighted in the attention matrix, could you explain the reason?\n4. The references are not sufficient, e.g., SFT, CoSeR, etc.\n5. As for the issues about uncoverage of full-image, why not consider employing the models for open word panoramic segmentation？\n6. Authors are prefer to emphasize the strength of the designed modules before explanation, e.g., Sec. 3.2.\n7. Typo. Wrong tense is used in line 179.\n8. There are some repeated references, e.g., 9 and 10,45 and 46.", "was_revised": true, "final_justification": "Most concerns have been addressed.", "rebuttal": "We sincerely thank the reviewer for recognizing several important strengths of our work in the **Summary** section, including:\n\n- the **plug-and-play design** of our spatially re-focused cross-attention module,\n- our **novel discovery** that traditional (vanilla) cross-attention induces hallucinations by diverting attention toward irrelevant pixels, and\n- the **effectiveness of our strategy** in mitigating interference from unrelated pixels.\n\nWe further appreciate the reviewer highlighting in the **Strengths** section that, based on our aforementioned **novel discovery/reveal** of a critical research gap, our proposed method can:\n\n- visibly **alleviate hallucinations**, and\n- significantly **improve the fidelity** of the super-resolved images.\n\nWe also appreciate the valuable feedback listed in the **Weaknesses** section, and we address them thoroughly in the responses below.\n\n## W1: Novelty\n\nWe appreciate the reviewer for raising this critical point about novelty. We are encouraged that the reviewer’s **originality** rating is “3: good,” and that all other reviewers gave either “3: good” or “4: excellent,” with explicit remarks such as “novel SRCA and STCFG mechanisms offer a unique contribution,” “STCFG introduces a novel use of classifier-free guidance in Real-ISR,” and “novel SRSR framework.”\n\nWe respectfully wish to clarify a key misunderstanding: our framework goes substantially beyond simply using segmentation masks as additional priors. Before elaborating on our specific novel contributions, we wish to provide a high-level summary that the novelty of our approach lies in **how we (1) design and (2) deploy the spatial prior**, addressing several under-explored and fundamental research gaps in text-conditioned Real-ISR.\n\n- **Design motivation**: Rather than using off-the-shelf segmentation masks as generic priors, our masks are constructed through *grounded* degradation-aware tags, and our entire framework is motivated by three systematically identified research gaps (Sec. 1, 3.1):\n    - **G1**: Diverted cross-attention (Novel finding that traditional cross-attention leads to hallucinations by diverting attention to irrelevant pixels.)\n    - **G2**: Incorrect prompts being extracted (Although many works attempt to develop better text extraction methods for Real-ISR, we identify that even state-of-the-art, degradation-aware prompt extractors can still frequently output incorrect tags due to heavily degraded LR inputs.)\n    - **G3**: Incomplete prompt coverage.\n- **Deployment**: Rather than simply adding segmentation as a conditioning signal, our modules **refine the core cross-attention and CFG mechanisms**, changing where and how the model focuses semantic information. This approach fundamentally differs from previous work and is specifically aimed at enhancing semantic accuracy, a largely under-explored challenge.\n\n### Specific Novel Contributions\n\n- **SRCA** (Spatially Re-focused Cross-Attention) directly addresses G1 and G2:\n    - G1: To our knowledge, **no prior work has explicitly re-designed the cross-attention mechanism itself to resolve its inherent limitations**. Prior methods typically focus on improved text prompts or additional conditioning, while continuing to use default cross-attention. Our work is motivated by our novel observation that unrefined cross-attention causes semantic hallucinations—an insight backed by new empirical evidence (see ablation and visualizations).\n    - G2: Instead of similarly exploring better prompt extractors, **we are the first to use the idea of grounding in this task to improve prompt accuracy further**.\n- **STCFG** (Spatially Targeted Classifier-Free Guidance) addresses G3:\n    - Simply using segmentation masks to guide restoration (as the reviewer assumed to be our design) can improve coverage; we have actually conducted ablation studies (Tab. 2) to prove the inferiority of this design. We have gone several extra miles, such as analyzing the limitations of using SRCA alone and other ablated versions. These motivated us to design STCFG, a **novel refinement of CFG tailored to Real-ISR**. It dynamically disables CFG in ungrounded regions, solving the accuracy-completeness trade-off, and leading to joint improvements in fidelity and perceptual quality.\n\n### Summary\n\nIn summary, our work is not about adding segmentation as a prior, but about **re-thinking how spatial priors can be designed and integrated to solve specific, under-addressed issues in text-conditioned super-resolution**. These deliberate design choices (guided by empirical discovery of new gaps) underpin the novelty and effectiveness of our SRSR framework. While our modules are deliberately simple and efficient, we believe their targeted, insight-driven design is what makes them both practical and genuinely novel.\n\n## W2: Length of Writing\n\nWe appreciate the reviewer’s suggestion to further improve the writing by ensuring that each section is appropriately proportioned. We are committed to making the paper as accessible and polished as possible. In response to this helpful feedback, we will revise the abstract to provide a more complete overview and streamline the first paragraph of the introduction to improve flow and readability.\n\n> “Existing Stable-Diffusion-based super-resolution approaches often exhibit semantic ambiguities due to inaccuracies and incompleteness in their text prompts, coupled with the inherent tendency for cross-attention to divert towards irrelevant pixels. **These limitations can lead to semantic misalignment and hallucinated details in the generated high-resolution outputs.** To address these, we propose a novel, plug-and-play *spatially re-focused super-resolution (SRSR)* framework **that consists of two core components: first, we introduce *Spatially Re-focused Cross-Attention (SRCA)***, which refines text conditioning at inference time by applying visually-grounded segmentation masks to guide cross-attention. **Second, we introduce a *Spatially Targeted Classifier-Free Guidance (STCFG)* mechanism that** selectively bypasses text influences on ungrounded pixels to prevent hallucinations. Extensive experiments on both synthetic and real-world datasets demonstrate that SRSR consistently outperforms seven state-of-the-art baselines in standard fidelity metrics (PSNR and SSIM) across all datasets, and in perceptual quality measures (LPIPS and DISTS) on two real-world benchmarks, underscoring its effectiveness in achieving both high semantic fidelity and perceptual quality in super-resolution.”\n> \n\nWe believe this revised abstract better balances clarity and completeness, and remain open to further improvements based on feedback.\n\n## W3: Explanation of Figure 1\n\nWe thank the reviewer for this observation. The regions not highlighted in the attention matrix correspond to areas that could not be confidently grounded by Grounded SAM using the input tag (in this case, “bird”). Since Grounded SAM operates directly on the low-resolution (LR) input, degradation in the image can cause it to be more conservative in assigning segmentation masks, resulting in certain relevant regions (such as the bird’s body) being left ungrounded.\n\nWe refer to such areas as **ungrounded regions** throughout the paper. We wish to highlight and reassure the reviewer that we have already analyzed this limitation in **Section 3.3** and specifically designed STCFG to bypass text influence in ungrounded regions, preventing hallucinations and reducing the negative impact of incomplete coverage.\n\n## W4: Additional References\n\nWe thank the reviewer for noting these related works, and will discuss both in the revision:\n\n- **SFT** demonstrates how spatially-varying categorical priors can be integrated into SR networks to recover more realistic textures.\n- **CoSeR** highlights the potential of bridging image and language understanding using diffusion-based priors and cognitive embeddings.\n\nWhile these methods use semantic or language guidance, our SRSR framework introduces a **grounded and spatially-refocused mechanism (SRCA + STCFG)** that directly tackles the challenge of semantic ambiguities caused by diverted cross-attention, inaccurate text prompts, and incomplete/ungrounded text prompts, which are not explicitly addressed by SFT or CoSeR.\n\n## W5: Why not use open-vocabulary panoramic segmentation models?\n\nWe appreciate this suggestion. In fact, we independently explored this exact idea and incorporated it into our work. Specifically, in Sec. 3.3, Sec. 4.3, and Supplementary Sec. B (Figures S12 and S13), we quantitatively and qualitatively evaluated Prompt-Free Anything Detection and Segmentation from DINO-X, a state-of-the-art open-vocabulary segmentation method.\n\nWhile this approach improves coverage over Mask2Former, its lack of degradation-awareness compromises accuracy on degraded LR inputs, leading to incorrect prompt-region associations and inferior SR performance (see Tab. 2, V8 vs. V4). Supplementary Figs. S12–S13 further illustrate these issues, which our method overcomes. We will clarify this in the revision.\n\n## W6-W8: Minor writing and formatting issues\n\nThank you for these helpful observations. We will address all the mentioned concerns in the revision:\n\n- **W6**: We will revise the methodology sub-sections accordingly by highlighting the strength of our proposed SRCA and STCFG modules before the detailed explanations.\n- **W7**: We will revise the tense in line 179 to maintain consistency.\n- **W8**: We acknowledge the duplicated references (9 and 10, 45 and 46) and will remove the redundancies in the updated bibliography."}, {"review_id": "GNrOnmO9EE", "reviewer": "Reviewer_u37U", "summary": "This paper introduces SRSR, a plug-in framework designed to improve semantic accuracy in real-world image super-resolution (Real-ISR). It features two key components: (1) Spatially Re-focused Cross-Attention (SRCA), which restricts cross-attention to semantically grounded regions defined by tag–mask pairs, and (2) STCFG, which mitigates hallucinations in ungrounded areas by adaptively modulating classifier-free guidance. SRSR is compatible with existing models without retraining. Experiments on demonstrate consistent improvements in image fidelity.", "questions": "**Motivation clarity**\n1. In Lines 37–38, you mention that DAPE “is only partially robust to severe degradations.” Could you elaborate on how this conclusion was derived? Is it based on specific failure cases, empirical observations, or quantitative results?\n\n**Perception–fidelity trade-off**\n\n2. In Fig. 7 of SUPIR$^{[1]}$, qualitative examples suggest that non-reference scores better reflect visual quality. In contrast, your work implies that improvements in reference-based metrics (e.g., PSNR, SSIM) better correlate with perceptual quality, and that gains in non-reference metrics may actually result from hallucinations. While I understand that this trade-off remains a challenging and open question in Real-ISR, and I appreciate the qualitative evidence you provide, there appears to be a contradiction with SUPIR’s conclusions. Could you offer further insight into this discrepancy? Specifically, do you see a path toward a more generalizable understanding of the perception–fidelity trade-off, beyond the current reliance on metric scores and qualitative examples?\n\n**Source of improvement: SRCA vs STCFG**\n\n3. In Section B, you show that replacing DAPE with semantic segmentation degrades performance in your framework. Could this be due to segmentation-based method has ungrounded regions where CFG can be selectively reduced? As noted in the weaknesses section, the marginal performance gap between V4 and V6 in Table 2 suggests that most of the gains may stem from CFG reduction (via STCFG), rather than from SRCA. Also, the segmentation-based variant yields better non-reference scores, though you suggest these may prefer artifact-heavy outputs. To clarify this: (1) A direct comparison between **(a) DAPE + Grounded SAM** and **(b) semantic segmentation, both with and without CFG reduction**, would help isolate the individual impact of each component; and\n(2) Further insight on the perception–fidelity trade-off (as discussed in Question 2) could support this claim.\n\n**Incorporating SRSR into training**\n\n4. In Section E, you suggest that integrating SRSR into the training process could lead to further improvements. Could you provide more intuition or preliminary evidence to support this claim? For example, would incorporating grounded attention during training help the model learn better semantic localization?\n\n[1] Yu *et al.* \"Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild\"", "limitations": "I would expect the authors to provide a deeper explanation for the significant drop in no-reference metrics, which are currently regarded as important indicators of performance in SD-based Real-ISR frameworks. This issue raises concerns about the practicality of the proposed method in real-world applications, where reference (ground truth) images are typically of low quality and no-reference metrics play a crucial role in evaluating perceptual quality.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strength**\n\n1. The paper is well-motivated, clearly identifying critical and unsolved issues in SD-based Real-ISR frameworks. The illustrative examples and experiments tightly support the claims.\n2. This paper is technically sound and clearly written.\n3. The proposed solutions are elegant:\n    - SRCA restricts cross-attention to semantically grounded regions (identified via Grounded-SAM), reducing the influence of irrelevant tags.\n    - STCFG introduces a novel use of classifier-free guidance in Real-ISR. Instead of manually tuning a global parameter, it disables CFG in ungrounded regions, reducing hallucinations.\n4. The plug-in framework enhances Real-ISR models without the need of retraining, making it efficient and practical.\n5. The experiments are comprehensive, and the qualitative results are particularly convincing.\n\n**Weaknesses**\n\n1. The non-reference scores drop significantly when SRSR is applied, raising the concern that the improvements may largely stem from simply reducing classifier-free guidance (CFG) through the proposed STCFG, rather than targeted semantic enhancement.\n    - For example, the ablation study (Table 2) shows that while STCFG substantially improves fidelity (from V2 to V6), the performance gaps between V6 and V4, as well as between V3 and V4, are marginal. This suggests that the gains may primarily result from reducing CFG strength in ungrounded regions, rather than from the semantic refinement provided by SRCA.\n    - To clarify this point, could the authors report the no-reference metric scores for the V2, V3, and V4 models?\n\n2. The grounding threshold described in Section 4.4 appears to act as another hyper-parameter that influences the perception–fidelity trade-off. While removing redundant tags is a reasonable strategy, its impact on performance is not clearly reflected in the results (e.g., the improvement from V1 to V2 is small).", "was_revised": true, "final_justification": "The authors' responses have addressed all of my concerns. The paper is insightful, well-written, and technically sound, contributing to the field of Real-ISR. Therefore, I would like to raise my score to 5 and suggest that this paper be accepted.", "rebuttal": "We sincerely thank the reviewer for their detailed evaluation and positive assessment of our work. We are especially encouraged by the reviewer’s recognition of our paper’s strengths across all four criteria: quality, clarity, originality, and significance:\n\n- **Motivation and identified research gaps**: “The paper is well-motivated, clearly identifying critical and unsolved issues in SD-based Real-ISR frameworks.”\n- **Novelty and methodological elegance**: “The proposed solutions are elegant,” and “STCFG introduces a novel use of classifier-free guidance in Real-ISR.”\n- **Comprehensive and convincing experiments**: “The experiments are comprehensive, and the qualitative results are particularly convincing,” “Experiments demonstrate consistent improvements in image fidelity,” and “The illustrative examples and experiments tightly support the claims.”\n- **Efficiency and practical merits**: “The plug-in framework enhances Real-ISR models without the need of retraining, making it efficient and practical,” and “SRSR is compatible with existing models without retraining.”\n- **Clarity and technical soundness**: Recognized by the numerical rating (“Clarity: 4: excellent”) and the encouraging comment: “This paper is technically sound and clearly written.”\n\nFinally, we appreciate the reviewer’s constructive feedback in the weaknesses and questions sections, and we address each point thoroughly in the responses below.\n\n## W1, Q2, Q3, and Limitation: Source of improvement and limitation of no-reference metrics\n\nGiven the close connections among W1, Q2, Q3, and the Limitation section, we address them together to provide a cohesive and structured response. We first disentangle the source of improvement between SRCA and STCFG (addressing W1 and Q3), and then offer further insights regarding the limitations of current no-reference metrics and the broader perception–fidelity trade-off (addressing Q2 and Limitation, which are also relevant to W1 and Q3).\n\n### **(1) Source of improvement: SRCA vs STCFG**\n\n> W1: Improvements may largely stem from simply reducing classifier-free guidance (CFG) through the proposed STCFG, rather than targeted semantic enhancement.\n> \n\n> Q3: Source of improvement: SRCA vs STCFG.\n> \n\nWe appreciate the reviewer’s thoughtful comments and agree that STCFG is the main driver of performance gains. However, we have noticed a slight but crucial misconception in W1 comments; thus, it is important to clarify that **the improvements from STCFG are not independent of targeted semantic enhancement—rather, both SRCA and STCFG contribute synergistically to this goal** (instead of SRCA solely contributing towards this). The reason why STCFG is more significant, in a nutshell, is that it effectively addresses the critical and widely observed shortcomings of grounding and SRCA (w/o STCFG) that are otherwise detrimental to semantic accuracy. Thus, it is important to highlight that such improvements are also for semantic accuracy. We will elaborate in the following paragraphs.\n\nSRCA refines semantic conditioning via more precise cross-attention maps, but in ungrounded areas, its effect in ungrounded areas is limited by the persistence of summary tokens (e.g., EOS) that capture semantics of all tags in the prompt (including the mismatched tags) and meaningless separator tokens, which can inadvertently introduce mismatched or noisy semantics. Note that masking these tokens is not feasible, as it disrupts the diffusion model’s noise prediction. \n\nMoreover, grounding (required for SRCA) reduces prompt coverage and increases ungrounded regions, especially for degraded LR images; thus, although SRCA alone has clear benefits, substantial room for improvement remains. \n\nSTCFG addresses the limitations of SRCA by replacing text-conditioned noise with unconditional noise in ungrounded regions, reducing hallucinations while preserving SRCA’s benefits.\n\nLastly, regarding the comparisons with segmentation-based methods in our ablation studies (V7 and V8 in Tab. 2), we wish to clarify that both versions already incorporate STCFG to ensure fair comparison with our final choice. Removing STCFG (see additional results: V9, V10) further degrades performance, highlighting its importance. With semantic segmentation (SS), the increased coverage reduces the frequency of ungrounded regions, so STCFG is less often triggered and brings smaller additional benefit than the setting without SS.\n\n| Version | PSNR↑ | SSIM↑ | LPIPS↓ | DISTS↓ |\n| --- | --- | --- | --- | --- |\n| V7 | **26.3128** | **0.7620** | **0.2725** | **0.2093** |\n| V9 | 26.2885 | 0.7609 | 0.2734 | 0.2098 |\n|  |  |  |  |  |\n| V8 | **26.3449** | **0.7627** | **0.2722** | **0.2089** |\n| V10 | 26.3221 | 0.7621 | 0.2729 | 0.2090 |\n|  |  |  |  |  |\n\n### **(2) Limitation of no-reference metrics and the Perceptual-Fidelity trade-off**\n\n> Q2: Perception–fidelity trade-off.\n> \n\n> Limitation: No-reference metrics.\n> \n\nWe appreciate the reviewer’s acknowledgment that this remains a challenging and open question in Real-ISR. The drop in no-reference metric scores is due to the same improvements in semantic accuracy (i.e., reduced hallucinations). No-reference metrics tend to over-reward artifact-heavy or hallucinated outputs, as noted in our paper and acknowledged by the reviewer. In contrast, full-reference metrics we employed also have dedicated measures for evaluating perceptual quality (LPIPS and DISTS) and fidelity (PSNR and SSIM). Our improvements in these metrics are well-aligned with the visual evidence we provide in the paper, such as Fig. 4, and Supplementary Material Figures S1-S17.\n\nIn SUPIR (Fig. 7), human preference can favor creative outputs diverging from the reference, but in Real-ISR, especially for applications needing semantic faithfulness (e.g., e-commerce product images), hallucinations reduce trust and utility. Thus, while SD-based SR models excel in producing creative results that are visually pleasing, an important question is that shall we reward creativity in Real-ISR as much as possible by pursuing favorable no-reference metrics results? Our view is that **faithful restoration, as measured by full-reference fidelity and perceptual quality metrics, should remain a central goal in many practical contexts**. For V2–V4, we focus on full-reference metrics: V3 (SRCA only) improves fidelity but reduces perceptual quality, while V4 (SRCA+STCFG) significantly improves both, aligning with qualitative evidence.\n\n**Path forward**: We fully agree that in scenarios lacking references, reliance on existing no-reference metrics is problematic, and developing improved, hallucination-aware no-reference metrics that better align with full-reference scores and human perception is an urgent research need. Promising directions include vision-language model-based assessment and metrics penalizing hallucinations. Nonetheless, in the standard settings of Real-ISR and similar settings when references are available, we advocate for full-reference metrics as a more complete and reliable evaluation. We appreciate the reviewer’s feedback and will incorporate these points in our revised Limitations section.\n\n## W2: The contribution of grounding\n\nThanks for raising this point. While grounding improves prompt accuracy by removing redundant tags, it also reduces coverage, increasing ungrounded regions. Thus, the modest gain from V1 to V2 reflects a suboptimal trade-off. Our STCFG module compensates for this limitation by improving quality in ungrounded regions. As a result, SRSR is robust to grounding thresholds (Tab. 3), consistently significantly outperforming the baseline.\n\nWe also wish to emphasize that grounding's contribution extends significantly beyond the performance improvements from V1 to V2. Crucially, it provides the essential tag–mask pairs necessary for enabling our SRCA and STCFG modules, without which these key innovations would not be feasible.\n\n## Q1: Motivation clarity\n\nThank you for this question. Our conclusion that “DAPE is only partially robust to severe degradations” is based on failure case analysis, empirical observations, cross-attention map analysis, and quantitative comparisons.\n\nFor reference, our study identifies three main research gaps (Sec. 1, lines 34–46 and Sec. 3.1, lines 109–131):\n\n- G1: Diverted cross-attention\n- G2: Incorrect prompts being extracted\n- G3: Incomplete prompts and coverage (relevant prompts not being extracted)\n\nWe observed hallucinated objects in the baseline linked to DAPE-extracted tags not grounded in the image (G2). Cross-attention maps confirmed their impact. We also found that DAPE sometimes misses relevant tags, leading to ungrounded regions and further hallucinations (G3). This is supported by qualitative and quantitative evidence throughout the paper and the supplementary materials. For example:\n\n- **G2 (Incorrect tags):** In Fig. 2 (last two rows), DAPE extracts the irrelevant tag “camouflage,” leading to hallucinations in the baseline. Similar cases appear in Supplementary Figs. S2 (“gun” and “rifle”) and S3 (“nose” and “mouth”), etc.\n- **G3 (Incomplete tags):** In Fig. S1, DAPE fails to extract the “claw” tag, resulting in an ungrounded region where the baseline hallucinates this area based on unrelated tags such as “animal.” A similar pattern occurs in Fig. S7, where missing the “plate” tag allows irrelevant tags to introduce artifacts.\n\n## Q4: Incorporating SRSR into training\n\nWe appreciate the reviewer’s interest in training-time integration of SRSR, which we briefly mentioned in our Future Works section. Our intuition is that incorporating grounding, SRCA, and STCFG during training would align the model’s learning with the spatial and semantic constraints imposed at inference, improving region-specific supervision and semantic localization. Integrating STCFG in training would also improve robustness to incomplete or noisy tags. We consider this a promising direction for future work."}, {"review_id": "OUWZ2sVmdm", "reviewer": "Reviewer_pFBn", "summary": "The paper proposes a novel Spatially Re-focused Super-Resolution (SRSR) framework for enhancing semantic accuracy in text-conditioned, diffusion-based real-world image super-resolution. The key idea is to refine text conditioning at inference time using visually grounded segmentation masks, which guide cross-attention (SRCA) and selectively disable classifier-free guidance (STCFG) for ungrounded regions. The approach is fully inference-time and plug-and-play, requiring no additional training. It outperforms seven state-of-the-art baselines in fidelity and perceptual metrics on both synthetic and real-world datasets.", "questions": "1. How does the performance change if the prompt extractor (DAPE) is replaced with a newer vision-language model or fine-tuned for specific domains?\n2. Could the authors clarify how sensitive SRCA and STCFG are to errors in the segmentation masks from Grounded SAM?\n3. Have the authors considered joint optimization between segmentation and SR to resolve interdependencies more robustly?\nScore could increase with answers addressing generalization and robustness to segmentation or prompt extraction failures.", "limitations": "Yes.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1.High quality of methodology and implementation, with strong empirical evidence supporting the claims.\n2.Clear ablation studies, hyperparameter analysis, and comparisons to state-of-the-art methods. Sufficient and reasonable experiments. Fig. 4 not only shows the superiority of the method, but also shows the limitations of existing IQAs.\n3.Novel spatially-aware cross-attention and CFG mechanisms offer a unique contribution.\n4.Strong plug-and-play usability makes it practical for integration.\nWeaknesses:\n1.Potential over-reliance on DAPE and Grounded SAM performance, which could vary in real-world applications. \n2.No user study or real-world deployment evaluation to validate perceptual benefits from an end-user perspective. Fig. 4 displays the limitations of existing IQAs, which further indicates the necessity of user study to evaluate the motivation.", "was_revised": true, "final_justification": "Thank you for the author's detailed and well-organized response. After reading it carefully, my response is as follows:\nRegarding the shortcomings of DAPE, the author carefully analyzes the two improvements to SRCA he proposed to illustrate its robustness. This analysis is undoubtedly convincing. However, I was curious about what would happen if DAPE were replaced with other visual text models, and the author did not provide a good answer to this question.\nFor Q2, the author's reply gave me a better understanding of the robustness of the proposed STCFG and SAM.\nFor Q3, I would like to thank the author for recognizing the joint optimization strategy I proposed and listing it as a possible future work direction. I agree with the author that the performance of the proposed method will be better with the development of backbones.", "rebuttal": "We sincerely appreciate the reviewer’s thoughtful evaluation and high-confidence endorsement of our work regarding several important aspects:\n\n- **Novelty and originality**: Recognized in the summary (“novel SRSR framework”), the strengths (“novel SRCA and STCFG mechanisms offer a unique contribution,” “high quality of methodology and implementation”), and the numerical rating (“Originality: 4: excellent”).\n- **Comprehensive and compelling experimental results that validate effectiveness and identified research gaps**: Praised for “strong empirical evidence supporting the claims,” “clear ablation studies, hyperparameter analysis, and comparisons to state-of-the-art methods,” “outperforms seven state-of-the-art baselines in fidelity and perceptual metrics on both synthetic and real-world datasets,” “sufficient and reasonable experiments,” and “Fig. 4 not only shows the superiority of the method, but also shows the limitations of existing IQAs.”\n- **Practical impact**: Highlighted by “strong plug-and-play usability makes it practical for integration” and “the approach is fully inference-time and plug-and-play, requiring no additional training.”\n\nFinally, we value the reviewer’s constructive feedback in the weaknesses and questions sections, and we have addressed each point thoroughly in the responses below.\n\n## W1, Q1, Q2, Q3: Generalization and robustness to segmentation and prompt extraction failures\n\n> Q3-2: “Score could increase with answers addressing generalization and robustness to segmentation or prompt extraction failures.”\n> \n\nWe are grateful to the reviewer for explicitly outlining actionable criteria (Q3-2) that could lead to a higher rating, which is helpful and actionable for us to prepare a targeted response that can more effectively address the concern. Noting that several comments (W1, Q1, Q2, and Q3) closely relate to this point, we address them together for a cohesive and structured response.\n\nWe first clarify both intuitively and with evidence, how our proposed SRCA and STCFG are robust to errors in prompt extraction (DAPE) and segmentation masks (Grounded SAM), thereby demonstrating that our framework does not overly rely on these components (addressing W1 and Q2). We then discuss potential future directions for improving these backbones, either by replacing DAPE (Q1) or by jointly optimizing the prompt extraction and segmentation (Q3-1).\n\n> W1: “Potential over-reliance on DAPE and Grounded SAM performance, which could vary in real-world applications.”\n> \n\n> Q2: “Could the authors clarify how sensitive SRCA and STCFG are to errors in the segmentation masks from Grounded SAM?”\n> \n\n### (1) Robustness to failures of DAPE\n\nFor reference, the three research gaps we identify in this work (Sec. 1, lines 34-46 and Sec. 3.1, lines 109-131) are:\n\n- G1: Diverted cross-attention\n- G2: Incorrect prompts being extracted\n- G3: Incomplete prompts and coverage (relevant prompts not being extracted)\n\nG2 and G3, both stemming from DAPE’s failures, often result in hallucinations in baseline methods. Our method is specifically designed to address these, thus being robust to them:\n\n- **Robustness to G2:** SRCA only retains tags that can be grounded and forms tag–mask pairs for attention re-focusing, thereby filtering out irrelevant tags wrongly extracted by DAPE. For example, in Fig. 2 (last two rows), DAPE extracts the irrelevant tag “camouflage,” causing hallucinations in the baseline, while our method removes its influence, as highlighted in the red box. Similar cases are presented in Supplementary Figs. S2 (”gun” and “rifle”) and S3 (”nose” and “mouse”).\n- **Robustness to G3:** Without SRCA, attention from existing tags can leak into ungrounded regions (e.g., backgrounds or salient objects missed by DAPE), resulting in hallucinated content. SRCA helps suppress this, but on its own cannot prevent global prompt tokens (e.g., EOS that captures the semantics of the entire prompt, consisting of all tags) from injecting semantics into ungrounded regions. STCFG further addresses this by blocking all text-conditioning (including EOS) in ungrounded regions. An illustrative failure case is shown in Fig. S1, where DAPE fails to extract the “claw” tag (highlighted in the red bounding box), leaving the region ungrounded. In this scenario, the baseline model (without SRCA and STCFG) hallucinates this area using unrelated tags such as “animal.” In contrast, our method (with SRCA and STCFG) prevents the attention of “animals” from influencing this region, resulting in a more faithful restoration. Similar examples can be seen in Fig. S7: when DAPE misses the “plate” tag, the corresponding region remains ungrounded. Baseline approaches allow irrelevant tags to introduce artifacts in such areas, while our framework effectively mitigates these errors and preserves semantic accuracy.\n\n### (2) Robustness to failures of Grounded SAM\n\nWe also observe failure cases where Grounded SAM does not provide comprehensive masks. Our method remains robust due to the design of STCFG, which improves faithfulness in ungrounded regions. Even if coverage is partial, SRCA ensures higher fidelity in grounded areas compared to the baseline. A visual example is shown in Fig. S1, where Grounded SAM fails to cover both “stone” objects in the image fully, and only the lower stone is confidently grounded, leaving a substantial ungrounded region. In the grounded area (yellow box), our method achieves higher fidelity. While the baseline hallucinates small pebble-like textures, our use of SRCA ensures that only the tag “animal” (and not “stone”) influences restoration, yielding more accurate results. For the ungrounded stone regions, STCFG effectively prevents over-synthesized or artifact-prone textures that the baseline exhibits, resulting in reconstructions much closer to the ground-truth HR image. Similar examples can be seen in Fig. 1 (Left). When the bird’s body is not fully grounded, STCFG prevents the introduction of artifacts from irrelevant tags such as “stone,” preserving the integrity of the ungrounded region.\n\nAdditionally, our comprehensive hyperparameter analysis in Sec. 4.4 and Tab. 3 demonstrate that our method is robust to varying thresholds for Grounded SAM, consistently outperforming the baseline.\n\n### (3) Potential directions\n\n> Q1: How does the performance change if the prompt extractor (DAPE) is replaced with a newer vision-language model or fine-tuned for specific domains?\n> \n\n> Q3-1: Have the authors considered joint optimization between segmentation and SR to resolve interdependencies more robustly?\n> \n\nWe appreciate these insightful suggestions. Following the baselines of SeeSR and OSEDiff, DAPE is adopted due to its proven advantage in Real-ISR in terms of degradation-awareness and efficiency, outperforming other off-the-shelf models like ResNet, YOLO, BLIP, and LLaVA. This is because DAPE is already fine-tuned on the Recognize Anything Model (RAM) for this domain, as suggested in Q1.\n\nNevertheless, as identified in G2 and G3 and previously discussed, there remains room for improvement. As discussed in the future work section (Supplementary Sec. E), we believe that advances in the prompt extraction and segmentation backbones can further improve SRSR’s performance. This is grounded on our observation from ablation studies that stronger semantic segmentation backbones (e.g., DINO-X over Mask2Former) lead to better results. Also, in line with Q3-1, we believe joint optimization of segmentation and SR is a promising and novel direction that could further enhance the current backbones. We are grateful that the reviewer endorses our comprehensive experiments and compelling results, which show that our current DAPE + Grounded SAM backbones already achieve consistent state-of-the-art results. As new advances emerge in these backbones, we believe our method will benefit from orthogonal improvements.\n\n## W2: User study\n\n> No user study or real-world deployment evaluation to validate perceptual benefits from an end-user perspective. Fig. 4 displays the limitations of existing IQAs, which further indicates the necessity of user study to evaluate the motivation.\n> \n\nThank you for highlighting the importance of evaluating perceptual benefits from an end-user perspective. In response to this motivation, we have conducted a user study to evaluate human perceptual preferences between the baseline results and those produced after integrating SRSR, details of which are included in Supplementary Material Sec. D and Fig. S18. In our study, we presented the low-resolution input for each image along with two anonymized high-resolution outputs (ours: SRSR-SeeSR and the baseline: SeeSR) labeled ‘Method A’ and ‘Method B’. Annotators were asked to select the preferred image or indicate if both appeared visually similar, with evaluations based on sharpness, visual realism, and detail preservation. The order of presentation was randomized, and full images were shown (not crops) to avoid bias. Results are aggregated and visualized in bar charts (Figure S18), showing that SRSR-SeeSR is consistently preferred over the baseline SeeSR across both datasets."}]

Author rebuttal:

Meta-review: The paper proposes SRSR, a plug-and-play framework that enhances semantic accuracy in text-conditioned Real-ISR through two key modules (SRCA and STCFG). Reviewers initially raised concerns about novelty, reliance on previous works DAPE and Grounded SAM, and the considerable drop in no-reference metrics. The rebuttal clarified the methodological contributions, degradation-awareness design, and the perception–fidelity trade-off, with additional ablation and metric analyses. All reviewers acknowledged the clarifications, raised their scores, and now recommend acceptance. Overall, this is a solid and practically useful paper with strong empirical results, and valuable contributions to the Real-ISR community.
Decision: Accept (poster)