Bridging Degradation Discrimination and Generation for Universal Image Restoration
Authors: JiaKui Hu
Zhengjian Yao
Lujia Jin
Yanye Lu
Venue: ICLR 2026 Poster (2026)
Keywords: Degradation Discrimination
Universal Image Restoration
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich detail. The challenges lie in sampling the distribution of high-quality images and adjusting the outputs on the basis of the degradation. This paper presents a novel approach, Bridging Degradation discrimination and Generation (BDG), which aims to address these challenges concurrently. First, we propose the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM) and demonstrate its effectiveness in performing fine-grained discrimination of degradation types and levels. Subsequently, we divide the diffusion training process into three distinct stages: generation, bridging, and restoration. The objective is to preserve the diffusion model's capability of restoring rich textures while simultaneously integrating the discriminative information from the MAS-GLCM into the restoration process. This enhances its proficiency in addressing multi-task and multi-degraded scenarios. Without changing the architecture, BDG achieves significant performance gains in all-in-one restoration and real-world super-resolution tasks, primarily evidenced by substantial improvements in fidelity without compromising perceptual quality.

Published as a conference paper at ICLR 2026
BRIDGING DEGRADATION DISCRIMINATION AND GENERATION FOR UNIVERSAL IMAGE RESTORATION
JiaKui Hu Zhengjian Yao Lujia Jin', Yanye Lu3.2,3
1 Institute of Medical Technology, Peking University
BBiomedical Engineering Department, College of Future Technology, Peking University "National Biomedical Imaging Center, Peking University "China Mobile Research Institute
ABSTRACT
Universal image restoration is a critical task in low-level vision, requiring the model to remove various degradations from low-quality images to produce clean images with rich detail. The challenges lie in sampling the distribution of high- quality images and adjusting the outputs on the basis of the degradation. This paper presents a novel approach, Bridging Degradation discrimination and Gener- ation (BDG), which aims to address these challenges concurrently. First, pro- pose the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS- GLCM) and demonstrate its effectiveness in performing fine-grat fine-grained discrimina- tion of degradation types and levels. Subsequently, we divide the diffusion train- ing process into three distinct stages: generation, bridging, and restoration. The objective is to preserve the diffusion model's capability of restoring rich textures while simultaneously integrating the discriminative information from the MAS- GLCM into the restoration process. This enhances its proficiency in address- ing multi-task and multi-degraded scenarios. Without changing the architecture. BDG achieves significant performance gains in all-in-one restoration and real- world super-resolution tasks, primarily evideneed by substantial improvements fidelity without compromising perceptual quality.
performin
INTRODUCTION
Image restoration aims to remove degradations from low-quality (LQ) images and to reconstruct high-quality (HQ) images while maintaining consistent semantic and texture details. In the context of deep learning (LeCun et al., 2015), image restoration can be further conceptualized tional generation task: employing LQ images as a condition, using neural networks to sample the distribution of the corresponding HQ images.
condi-
Universal image restoration (Luo et al., 2023; Zheng et al., 2024; Hu et al., 2025a) represents an emerging and significant subfield of image restoration, which is intended to challenge the restora- tion model to effectively identify a myriad of complex or previously unseen degradations. This requires that the restoration model possesses two capabilities:
(1) degradation discrimination and (2) conditional generation. The former propels the model to discern the degradation present in input images, thereby enhancing the model's adaptability, whereas the latter enables the model to gener- ate the HQ images based on the LQ images, fulfilling the restoration. These two capabilities lead researchers to develop universal image restoration models from two distinct perspectives. Several methods (Li et al., 2022a; Zhang et al., 2023a; Potlapalli al., 2023; Marcos V. Conde, 2024; Hu et al., 2025a) employ additional degradation discrimination networks (or parameters) to guide the model in identifying the degradation. This approach has demonstrated efficacy in all-in-one image restoration tasks. However, it results in over- outcomes due to the fidelity- foccued learning objectives, and may not perform well in real-world scenarios. In contrast, Wang et al. (2024); Yu et al. (2024); Wu et al. (2024); Lin et al. (2024); Chen et al. (2025) focus on effectively exploiting generation prior of the pre-trained generation model output rich textures. These methods have
"Corresponding author.

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Published as a conference paper at ICLR 2026
proven effective in real-world and photo-realistic image restoration tasks. However, for the all-in- one image restoration task, these methods struggle to produce detailed content consistent with the LQ image. This issue potentially arises from the diffusion model erroneously interpreting mildly degraded images as severely degraded, compelling it to generate rich but inconsistent detail, thereby causing less fidelity.
To maintain the generation prior of the diffusion model while improving its restoration fidelity in other common tasks, we propose Bridging Degradation discrimination and Generation (BDG). The essence of BDG lies in the seamless integration of fine-grained degradation discrimination capabili- ties with robust high-quality image generation within a single model. This configuration enables the model to effectively address degradation presented in diversified or complex forms and subsequently produce HQ images.
For degradation discrimination, we employ Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM) to distinctly identify complex and diversified degradations. Through visual- ization, T-SNE clustering, and KNN classification, practically demonstrate that our MAS-GLCM surpasses previous degradation characterizations, egg., gradients (Ma et al., 2020), frequency (Ji et 2021), parameters (Potlapalli et al., 2023), and instructions (Luo et al., 2023; Marcos V. Conde, 2024), in advanced fine-grained degradation discrimination. Based on this finding, BDG utilizes to endow the model with degradation discrimination abilities.
For bridging degradation discrimination and generation prior, we design a three-stage diffusion method by modifying the parameters in the diffusion reserve formula. (I) During the generation stage, the model incrementally captures pixel dependency through a denoising process. (II) In the bridging stage, the model incorporates residual information as an input condition. The inherent degradation discrimination capacity of the residual (Tang et al., 2024a) provides advantageous con- ditions for the introduction of fine-grained degradation discrimination ability. Accordingly, we accomplish BDG by aligning the GLCM features with the diffusion features in the bridging stage. (III) In the restoration stage, the focus of the model shifts from generating HQ images to prioritizing training in restoration ability. During this stage, continued alignment of the GLCM features with the diffusion features necessary. This alignment ensures that the model's fine-grained degradation discrimination ability can be retained. After firmly bridging the degradation discrimination and the generation prior, we attain superior restoration performance in both all-in-one image restoration and real-world super-resolution. thereby illustrating BDG's effectiveness.
The proposed BDG framework facilitates the precise attainment of high fehh-fideitt,, universal restora- tion model that effectively accommodates arbitrary degradation arising from the image generation model. By capitalizing on the nuanced degradation discrimination capabilities of MAS-GLCM, coupled with the integration of robust pre-trained generative model, models trained within the BDG paradigm achieve an optimal equilibrium between content fidelity and detail restoration in the context of image restoration tasks.
RELATED WORK
Degradation diversities in restoration. To enhance the adaptability of restoration models, searchers have initiated studies that aim to develop a single model capable of addressing multiple restoration tasks, process known as all-in-one restoration (Li et al., 2022a). In this context, the restoration model is expected to effectively restore input images with various degradations. Numer- ous methods (Li al., 2022a; Potlapalli al., 2023; Zhang et al., 2023a; Luo et al., 2023; Marcos V. Conde, 2024; Hu et al.. 2025a;c) are designed to improve all-in-one restoration performance by introducing the degradation discrimination capability. AirNet (Li et al., 2022a) uses MoCo (He et al., 2020), while IDR (Zhang et al., 2023a) creates physical degradation models for identifying degradations. PrompuR (Potlapalli et al., 2023) incorporates additional parameters through dy- namic convolutions to enable universal image restoration without relying on embedded features. DCPT (Hu et al., 2025a) approaches the restoration model as a degradation classifier to encourage it to learn about the diversity of degradation. In contrast, (Wang et al., 2023b; Zheng et al., 2024; Qin et al., 2024) aim to allow the model to extract features independently of degradation, ensuring that its output is solely linked to the intrinsic distribution of the images.
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Generation priors for real-world restoration. To improve the applicability of restoration models in real-world settings (Wang et al.), researchers have begun to incorporate generation priors into these models. Pre-trained in high-quality real-world images. large-scale image generation mod- els (Esser et al., 2021; Rombach et al., 2022; Peebles & Xie, 2023; Tian et al., 2024; Esser et al,, 2024; Liu et al., 2024) are considered proficient in fitting complex image distributions. Existing real-world restoration techniques (Kawar et al., 2022: Fei et al., 2023; Wang et al., 2024; Wu et al., 2024; Yu al., 2024; Lin al., 2024; Yao et al., 2026) attempt leverage this capability of pre- trained large-scale image generation models to address scenarios involving intricate image distribu- tion challenges. DDRM (Kawar al., 2022) is the pioneering method for employing the generative prior of diffusion model (Ho et al., 2020), thus markedly enhancing the perceived effectiveness of the restoration model. GDP (Fei et al., 2023) attributes these improvements offered by pre-trained generative models to their inherent general image priors. StableSR (Wang et al., 2024) capitalizes on the generative prior of stable diffusion (Rombach et al., 2022), leading to a substantial enhance- ment in the perceived effectiveness of the restoration model in real-world scenarios. DifIBIR (Lin et al., 2024) expands this capability to include a variety of blind image restoration tasks. SUPIR (Yu et al., 2024) advances the field by making significant contributions to large-scale restoration models through the integration of scaling. SeeSR (Wu et al., 2024) explores how high-level semantics can better assist diffusion- baasdd restoration. PURE (Wei et al., 2025) also successfully used pre-trained autoregressive MLLM (Hu et al., 2025d;b) to adapt to real-world super-resolution.
METHODS
3.1 DEGRADATION CHARACTERIZATION
Methods utilizing degradation characterizations Ma et al. (2020); Ji et al. (2021); Potlapalli et al. (2023); Luo et al. (2023); Marcos V. Conde (2024) have been widely demonstrated to enhance restoration performance under various degradations. Existing degradation characterizations are tied to image content, e.g., the Sobel operator (Ma et al., 2020) focuses the texture at edge. When used to align with the restoration network, the network predominantly aligns with image content, thus inhibiting the ability to capture degraadtion-specifii information.
To achieve more refined degradation characterization that is minimally affected by image content, we introduce the MAS-GLCH and substantiate its proficiency in discriminating degradation. MAS- GLCM is designed on the basis of GLCM, which serves as an effective extractor of image texture, depicting the texture characteristics of an image by evaluating spatial associations between pixels at different gray levels. Specifically, the GLCM constitutes a matrix that computes the frequency with which pixel pairs of given gray levels co-occur within an image. Each matrix element quantifies the occurrences of one gray value in conjunction with another at specified distances and orientations. Since its computation does not involve processing the image's content, GI.CM's result inherently discards information about the image content. Given a gray image I E H its GLCM M can be expressed as follows.
1, if [x,,)) = and
W H
(i..A
1(x + Az, y Ay) =
0, otherwise
where Ax, Ay denotes the distances selected in the horizontal and vertical directions.
MAS-GLCM. Our objective is to determine the average value of GLCM in various groups of Az, Ay, to fully extract information that encompasses multiple angles and scales, thereby extracting
degradation-relater information at multiscales and avoiding being trapped in locality. Considering
Az, Ay can be formulated by the angle & and the module as Az = 1 sin(1). Ay = 1- cos (
Given multiple and I, we compute the average value of their M to obtain our MAS-GLCM M.aas which can be formulated as follows.
L,8 Σ
Mmas
M Me.ss((e))
(2)
n X m
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low
Degradation Level
high
(a) LQ images
Down Sample
Hooee
KL divergence: 0,858452
Gaussian
(b) GLCM
Noise
Low
Light
Kl. divergence: 0.263613
Visualization.
(2) Clustering analysis.
Figure (1) Visualization of MAS-GLCN in varying degradation levels. With an increase in degradation levels. the MAS-GLCM exhibits sigmificant transformations. (2) The results of the T- SNE analysis for LQ images and MAS-GLCM across various degradation types demonstrate that MAS-GICM possesses an enhanced capacity to distinguish between degradation types.
where I, represents different scales I. 1,, 12,12,... ... la and represents different angles θ = 01, 02,,'. 0m
Fine-grained degradation discrimination. We demonstrate that Myas has ane-grained degrada- tion discrimination capability through visualization and clustering analysis, shown in Figure
Visualization. Figure (I) displays the MAS-GLCM at various degradation levels. We lect a clean image with three simulated degradation types: downsampling, Gaussian noise, and low-light. For each degradation type, five distinct levels defined, details in Ap- pendix A. The visualization indicates that MAS-GLCM significantly changes with varying degradation levels.
Clustering analysis. Figure 1 (2) illustrates the T-SNE results associated with the degrada- tion cluster using LQ images and MAS-GLCM. Following T-SNE clustering, MAS-GLCN exhibits much lower KL divergence compared to LQ images, indicating superior cluster- ing efficacy. More results on real-world datasets are shown Appendix A. This claim is further supported by the visualization of the clustering results.
Classification analysis. Table compares the degradation characteristics in classifica- tion tasks for type and level, including haze, low light, snow, Gaussian noise, and Pepper noise, with Gaussian noise variances of 15, 25, 50, 75, and 100, using a KNN classi- fier (details in Appendix A). The classification results demonstrate that MAS-GLCM shows superior performance, especially in the fine- grained degradation level classification task.
Degradation
Type Acc (%)
Level Ace (%)
Characterization
LQ images
51.44
20.00
Sobel (gradient)
40.80
23.33
Laplace (gradient)
83.05
20.83
Fourier
65.80
30.83
MAS-GLCM (Ours)
97.13
74.17
Table 1: MAS-GLCM has substantial capability in the classification of both types and levels of degradation.
3.2 CONDITIONAL GENERATION
The diffusion model has been widely proven to have superior generative capacities (Rombach et al., 2022). Their inherent generation ability (Fei et al., 2023) significantly helps the model in addressing restoration tasks in real-world degradation. Following (Wang et al., 2024; Zheng et al., 2024; Wu al., 2024), use the diffusion model to learn how to fit the distribution of HQ images.
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Condition mechanisms. In contrast to image generation tasks, image restoration tasks possess pronounced condition the form of the LQ image, which serves as a guidance for the model. Com- mon techniques for incorporating such conditional information in diffusion- basdd image restoration models include methods such as Cross Attention, ControlNet (Zhang et al., 2023b), residuals ( (Yue al., 2023; Liu et al., 2024), etc. Following (Liu et al., 2024; Zheng al., 2024), we use residuals and LQ images or their latents as conditions.
In nutshell, the forward process of the diffusion model is follows.
Tt It-1 + Octires BBE 6,33gq
where is the diffusing result in timestep i, Tres == Fig Thg is the residual of the IQQ image (or its latent) Bi and the HQ image (or latents) Thg. at Br, and 6g is the noise coefficient of Tress standard Gaussian noise and Tig- respectively.
In the sampling process, we omit the noise term to change this diffusion to an implicit probabilistic model (Mohamed & Lakshminarayanan 2016). The derivation can be found in Appendix B.
32 B
It-1 = 24 OrF
8,314
(4)
Discussion. Eq. 4 is the basis for us bridge the degradation discrimination and the generative prior. As derived from Eq. 4, the image generation restoration capability of this diffusion model governed by three parameters: a, 8, and 5. We shall examine the performance of this diffusion model in the subsequent three cases:
Lt This
1. Generation. a, = 0 and 6, = O, Eq. 4 will degenerate into It-1 =
formula is formally equivalent the denoising formula of the Variance Exploding (VE) SDE (Song et al., 2021). The model in this stage only has generation abilities, as has not been provided with the necessary conditions for restoration, such Tres or 3ig-
2. Bridging. Only 8, = 0, Eq. will degenerate into Tz-1 I2 a12
B
E The
B,
diffusion model capable comprehending degradations by leveraging the degradation- aware information (residual Tree (Tang al., 2024b)), while preserving its generation prior.
3. Restoration. All parameters are scheduled as normal, Eq. 4 does not degenerate. Note that we set a # 8G, so the introduction of 21g will not be diluted by Ires. Due to direct injection of Fig. the predicted image zg can have stronger fidelity. Different from the DiffUIR (Zheng et al., 2024), sampling of BDG predicts both noise and residual to obtain the generation (noise prediction) prior, whereas DiffUIR only predicts residual, missing out acquiring the generation prior diffusion models.
3.3 TRAINING
The BDG training phase can be divided into three distinct stages, each corresponding the cases previously discussed. This three-stage methodology is intended to enable the restoration model not only to retain the generation model's capability for recovering detailed textures, but also to acquire enhanced knowledge pertinent to degradation. This approach is designed to improve the model's adaptability to varying task requirements and degradation scenarios. Figure 2 clearly shows this training process. We introduce each stage sequentially.
Generation Pre- tre-training. We set the coefficients a, = and d, to correspond to the generation stage of the diffusion model. At this stage, the model mainly assimilates the generation prior from extensive and high-quality image datasets.
Bridging stage. We maintain 8, 0 in this stage. A primary objective of this stage is to preserve the model's generation capabilities. Specifically, conditional on Eq. 4, is imperative that the model accurately predicted the distribution po p (x:)) based on g (2t,, To, Tresy Fin).
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A-1
1r-1
Degradation ClassiBcctioo
7
6,11g
Align
MLP
MLP
Alige
MEP
Full-negarive Cuntrastion
GLCM
GLCM Encoder
Denoising
Denoising
Deneising
GLOM
1 GLOM
Genention Pro-training
Bridging Degrddation discrimination and Generation price.
Restonatoon Fine-tuning:
Three training stages in BDG. (1) During the generation stage, the model focuses on ob- taining generation priors. (2) In the bridging stage, the MAS-GLCM, which can identify degradation fine-grainedly, is aligned with the features of the pre-trained generation model, thereby endowing the model with initial capabilities in degradation discrimination. (3) In the restoration stage, the model is tasked with performing restoration.
Figure 2:
ggen DKLLa((2-1 1/ 20: Tress (in) (++-1
E [z) [llu(ze, - uo 1
(5)
8
EEgeirrr | Irea) + (e
3
Next, effectively integrate the fine-grained degradation classification capability with the genera- tion process, we introduce a novel eegrrddttiongenerrati bridging strategy. Specifically, a neural network is utilized extract the abstract features MAS-GLCM Mmaas Given that Mmas demon- strates a robust fine- eeaegga degradation classification capability, its high-dimensional features F Fmas are considered embody this capability. Furthermore, these features are aligned with the interme- diate features of the Diffusion Model Fassy- The loss function is as follows.
Cbridge 2 E[ m2d (F (Fmas), pm22d ( (F Fmaa))++
(6)
H(y d2m (Faff)) d2m (Fais1)))s
where pm2d((mma) is the soft-maxed similarity between F Fmas and Fasfs, m2dd denotes the one-hot ground-truth similarity of Fmas. and the cross-entropy function.
Finally, it is imperative employ a loss function to ensure that Fmas has fine-grained degradation classification capabilities. As illustrated Figure (2), an additional MLP used to process F Fass which is then optimized using the loss of degradation classification.
Ldeg-ols ((MLP(Fmms), C),
where C is the one-hot degradation class.
In real-world scenarios, degradation challenging to categorize into distinct classes. A sound approach emulate real-world degradation involves the fusion of simple degradations in multiple orders (Wang et al.; Zhang et al., 3021). For example, Real-ESRGAN (Wang et al.) exemplifies this by accumulating four types degradation: blur, downsampling, JPEG compression, sinc artifacts and noise, iteratively superimposed eight steps. Each step in this chain represents an increased
We select the first-layer feature of the UNet decoder as Fasst-

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level of degradation complexity. Accordingly, we define eight intermediate states (e.g., after the first operation, second, etc.), which serve as pseudo-labels indicating the stage (or "order") of degradation application. These orders act surrogates for degradation severity and compositional complexity. By training the model to recognize these order levels, it learns to implicitly estimate how heavily an image has been degraded, enabling better adaptation to varying degrees of real-world distortion.
This provides a more feasible and meaningful learning signal than attempting to assign discrete type labels that may not reflect actual conditions for real-world super-resolution task. This task termed "order classification", and its associated loss function can be derived by replacing C' in Eq. 7 with the one-hot order class.
In summary, the loss function at this stage is as follows.
Lidg Lgen A (Erridge + Ldeg-ola):
(8)
where À balances these losses and is set 0.1 by default.
Restoration Fine-tuning. Subsequent to the bridging stage, is imperative to enhance the fidelity of predicted images and the fine-grained degradation discrimination ability. Specifically, is crucial to ensure that the gt predicted by the diffusion model aligns closely with the ground truth Tge while allowing the features of the diffusion model accurately discern degradation using Coridge. Thus, we define ft as follows:
fr 1a Fgg XLrridge.
In real-world scenarios, the degradation observed images is distinctly different. To improve the model's 's ability to recognize degradation, we reframe the degradation classification problem during the bridging stage as a contrastive learning task that involves only negative samples ("full negative contrastive learning"".. Negative samples are those exhibiting different types or levels of degradation, and our goal to extend the distance between pretrained MAS-GLCM's features Fmas for each negative sample.
fentt -E cos F Mass Få F)
(10)
AEB JEB
f3) denotes the cosine similarity between vectors f and fd. Within real-world super resolution task, L,f: 1| Z + A(Coridge Lfent)
where cos( (
is important to note that we do not implement this loss during the bridging stage. the bridging stage, the feature extractor of Mmas is still training, and full negative contrastive learning would result a representation collapse (Hu et al., 2021). In contrast, in the RFT stage, the weights of the feature extractor are frozen, rendering the model immune to the effects of representation collapse.
EXPERIMENTS
We perform an evaluation of our BDG across three distinct restoration tasks. (1) All-in-one: a model
trained to restore images in multiple degradation, including real-world scenarios. (2) Mixed degradation: model restores images affected by composite degradation. (3) Real-world: real- world super-resolution task also used to test BDG.
In the all-in-one and mixed degradation image restoration task, employ a 36M UNet pre-trained on ImageNet. the real-world super-resolution task, Stable Diffusion 2 (Rombach et al., 2022) utilized as baseline without incorporating additional architectures such as cross-attention or control-net. The implementation details are: batch size 256, learning rate x 10-4, and AdamW optimizer with (81, (2) (0.9, 0.95). For each task, train 300k iterations. The bridging stage and the restoration fine-tuning stage each have 150k iterations.
Detailed datasets, metrics, and qualitative results are provided in Appendix C.

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4.1 ALL-IN-ONE IMAGE RESTORATION
We train a 5D all-in-one image restoration model with simulated dataset following DiffUIR (Zheng al., 2024). This model is validated on simulated and real-world scenarios.
Deraining (6sets) SSIM
Enhancement PSNR
Desnowing
Dehazing
Deblurring (4aels)
Method
PSNR
SSIM
PSNR
SSIM
PSNR 1
SSIM f
PSNR 1
SSIM
Prompt-IR
29.56 36
0.888
22.89
0.847
31.98
0.924
32.02
0.952
27.21
0.817
DA-CLIP
28.96
0.853
24.17
0.882
30.80
0.888
31.39
0.983
25.39
0.805
DiffUIR-L
31.03
0.904
25.12
0.907
32.65
0.927
32.94
0.956
29.17
0.864
InsturetR::
31.35
0.911
24.33
0.887
32.71
0.993
32.08
0.957
29.58
0.874
RAM-PrompdRft
32.17
0.914
24.88
32.75
33.79
0.976
29.76
0.871
DCPT-PrompdRt
32.29
0.921
25,39
0.893
32.79
0.941
32.94
0.956
30.32
0.888
BDG (Ours)
34.75
0.974
27.42
0.930
32.86
0.950
34.33
0.993
31.11
0.904
Table All-in-one Image Restoration results. means the methods are retrained within datasets we used for fair comparison. The best and second results are shown in red and blue respectively.
Input
Daclip
Instructir
DCPT
BDG (Ours)
Target
Figure 3: Visual comparison on the all-in-one image restoration task. From top to bottom, each row corresponds to: deblurring, low-light enhancement, and deraining.
Results all-in-one task are reported in Table 2. Our BDG attains the State-of-The-Ar (SoTA) performance across all tasks. Compared to DiffUIR (Zheng et al., 2024), which employs the same architecture and a similar diffusion sampling process, significant improvements are realized, mea- suring 3.72 dB, 2.30 dB, 1.39 dB, and 1.94 dB for deraining, low light enhancement, dehazing, and deblurring, respectively. In contrast to the recently leading method DCPT (Hu et al., 2025a), an enhancement of 2.46 dB is also observed in deraining. In particular, the restoration fidelity of the large-scale unified visual generation model (Wang et al., 2023b) is inferior to that of the model specifically trained for restoration.
Degradation
Snow
Haze
Low-light
Method
PIQE JIQE///BR BRBRQQUE
PIQE BBISQQEE
PIQE L/BRISQUE
DA-CLIP
31.34 / 24.45
47.67 / 34,90
37.64/ 27.45
Instruct|R
33.35 24.41
50.97 / 31.45
36.08 26.31
DCPT-NAPNet
32.59 25.002
52.40 37.97
35.48 26.97
UniRestore
32.69 / 27.16
46.88 / 30.95
34.63/ 27.05
FoundIR
33.18 / 26.20
61.14 / 42,26
44.17/33.51
BDG (Ours)
31.45 / 24.00
47.59 / 34.75
34.44/ 27.41
Table 3:
Real-world restoration results in four real-world degradation types under the zero-shot setting. The best and second results shown in red and blue respectively.
Results in real-world scenarios are reported in Table 3. According to these quantitative metrics, BDG attains the majority of the best and the second-best results, notably achieving the lowest PIQE in the low-light enhancement task. In other real-world degradations, BDG also achieves the lowest or the second-lowest PIQE and BRISQE, demonstrating its robustness. In comparison to diffusion- based methods lacking degradation identification (Zheng al., 2024; Li et al., 2025), BDG demon- strates enhanced fidelity while maintaining the detailed texture restoration prowess inherent to the generation model.
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Input
Daclip
Instructir
FoundIR
UniRestore
BDG (Ours)
Figure 4: Visual comparison on the real-world all-in-one image restoration task. From top to bottom, each row corresponds to: desnowing and low-light enhancement.
4.2 IMAGE RESTORATION UNDER MIXED DEGRADATION
We also train and test our BDG for the mixed degradation scenarios (Guo et al., 2025).
IDDDI-Double
IDDII-Trrppe
Method
L+H
L+R
L+S
H+R
H+S
L+HH+
L.+H+S
PromptR
24.49
.789
25.05
.771
24.51
.761
24.54
.924
23.70
.955
23.74
.752
23.33
.747
WGWSNet
24.27
25.06
.772
27.23
.955
27.65
.60
23.90
.772
23.97
.771
WeatherDiff
21.83
.756
22.69
.730
22.12
.707
21.25
.868
21.99
868
21.23
.716
21.04
.698
OneRestore
25.79
822
25.58
799
25.19
.789
29. 99
.557
30.21
964
24.78
.788
24.90
.791
McCE-IR
26.25
800
29.93
.964
30.19
BDG (Ours)
27.27
26.67
.817
26.59
34.21
.975
34.42
.979
26.14
.809
26.45
.809
Table 4: Comparison to state-of-the-art on composited degradations. The best and second results are shown in red and blue respectively.
Results in mixed degradation scenarios are reported in Table 4. Compared the previous SoTA method (Zamfir et al., 2025), BDG demonstrates substantial performance improvements across all mixed degradation scenarios, with particularly notable improvements in scenarios characterized by haze and rain (H+R) degradations, where the enhancement reaches 4.28 dB.
4.3 REAL-WORLD REPEERRESOOLIION
We conduct experiments real-world super-resolution
ESRGAN
(Oers)
PSNR 1
21.87
21.94
20.85
20.84
18.68
21.19
20.94
20.77
21.26
21.75
24.1977
SSIM
0.5539
0.5736
0.5103
0.4887
0.4664
0.5385
0.4938
0.4958
0.5239
0.5422
0.6241
0.3843
DISTS
0.2737
0.2601
0.2428
0.2542
0.2207
0.2257
0.2471
0.2538
0.2500
0,2606
0.2571
D/V2K- Vol
FID
64.28
$3/46
53.7
36.57
32.18
31.93
40/42
40.77
41.93
$5.77
43.49
MANIQA
0.4834
0.5251
0,4699
0.5914
0.5491
0.6198
0.6205
0.6049
0.5237
0,5232
0,5066
61,2826
CLIPIQA
0.5183
0.5424
0.5597
0.6486
0.6035
0.6946
0.6064
0.6799
0.5695
0,5948
0.6396
PSNR
28.75
28.64
26.9
28.13
21.41
28.17
26.76
27
27.98
28.46
28.7961
SSIM
00031
88853
0.7572
0.7542
0.6696
0.7691
0.6576
0.7084
0.7453
0.7673
0,8039
1PIIS
0
0.3189
0.4599
0.2089
0.2515
MANIQA
0.4878
0.4907
0.442
0.5591
0.457
0.6042
0.5923
0.585
0.5043
0.4586
0.4899
MUSIO
57.14
$0.18
$3.74
58.42
64.53
61.93
61.19
61.81
53.73
$0.5
58,7432
CLIPIQA
0-1915
0.4622
0.5461
0.6206
0.58
0.6804
0.6346
0.6773
0.5706
0.53-12
0,6053
22.67
25.18
24.77
SSIM
0.7654
0.7616
0.7358
0.7085
0.6567
0.7216
0.6572
0.663
0.7148
0,7421
0.7509
LPIPS
0.267
0.2727
0.2942
0.3002
0.3515
0.3019
0.3658
0.3435
0.318
0.346
0,3016
ReadSR
DISTS
0.2121
0.2063
0.2288
0.2139
0.2385
0.2223
0.231
0.2259
0.2213
0.2198
0.2574
MANIQA
0.5399
0.5346
0.6253
MUSIQ
60.18
69.77
CLIPIQA
0.5001
0.4449
0.527
0U6178
0.5171
0.6612
0.6386
0.659
0.5709
0.5414
0.6332
Table 5: Real-world super resolution results on synthetic and real-world benchmarks. The best
and second best results of each metric in diffusion-basse methods are highlighted in red and blue, respectively.

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Published as a conference paper at ICLR 2026
Results in real-world super-resolution are shown in Table 5. We have the following observations. (1) Our BDG consistently scores the highest or second highest in PSNR, SSIM, and LPIPS across all datasets.
(2) BDG shows notable improvement in fidelity. In DIV2K-Vas Val, BDG outperforms 2.45 dB in comparison to the second-best diffusion method and all GAN-based methods in PSNR. This huge enhancement is because liffusion-based methods often generate textures that deviate from the ground truth, putting the results at a disadvantage in full-reference metrics. In contrast, BDG closely aligns the output with LQ images through Eq. securing favorable results in full-reference metrics. (3) In non-reference metrics such as MANIQA, MUSIQ and CLIPIQA, BDG outperforms its baseline (Rombach et al., 2022) and ResShift ( (Yue al.. 2023). BDG effectively informs the model about the type or level of degradation, avoiding the model from creating textures that are inconsistent with GT at lower levels of degradation. Overall, BDG excels in full-reference metrics while being competitive in non-reference metrics.
4.4 ABLATION STUDY
The results of the aforementioned experiments generally prove that BDG leads to a significant gain in restoration performance. In this subsection, we mainly analyze the impact of different components in BDG on the restoration results. We perform ablation studies on 5D all-in-one image restoration and real-world super-resolution.
Bridging
RFT PSNR / SSIM
Bridging
RFT
PSNR / SSIM CLIPIQA
300k
0
30.25 / 0.871
300k
0
28.35 / 0,7988 / 0.5839
0
300k
31.03/ / 0.908
0
300k
28.73 0.8093 / 0.4787
150k
150k
32.09 / 0.950
150k
150k
28.80 0.8039/ 0.6053
Table 6: Ablation of training stages on all-in-one restoration task (left) and real-world super-
resolution task (right).
Impact of training stages. As shown in Table 6, the optimal restoration performance is achieved when both the bridging stage and the RFT stage are present.
Curitge Eiot
PSNR/SSIM CLIPIQA
Ogen Caridge Litegets
PSNR SSIM
x
x
27.57/ 005839
x
x
31.11 / 0.883
v 28.23 0,7988 / 0,5935
x
20.88 / 0.847
27.88 0.7844 0.5589
32,09 / 0.950
28.80 0.8039 / 0.6053
Table 7: Ablation of losses in the bridging stage (left) and the RFT stage (right).
Impact of losses. As demonstrated in Table 7, the loss functions that we have developed for the bridging stage and the RFT stage result in improvements in restoration fidelity while maintain- ing perceptual integrity. The performance of the model deteriorates markedly when reliance is not placed on Idde--cs- We contend that in the absence of Lete--cls- the MAS-GLCM encoder is devoid of discrimination objectives, leading to a model collapse issue. Consequently, the diffusion features become aligned with the collapsed MAS-GLCM encoder, resulting suboptimal results. In con- trast, with only Lignn the collapsed MAS-GLCM encoder does not adversely impact the restoration models, thus still achieving a certain degree of restoration performance.
5 CONCLUSION
This paper presents Bridging Degradation discrimination and Generation (BDG) for universal image restoration. The BDG approach proficiently enhances the model's 's capability to perform restoration contingent upon type or level of input degradation while effectively leveraging the generative prior to enrich the detail and texture the output image. Specifically, we design MAS-GLCM to finely identify the degradation. Subsequently, by reformulating the diffusion backward process equation, we design a three-stage diffusion training method. It endows the model with the ability to dis- cern degradation while preserving its capacity generate superior texture details by aligning the MAS-GLCM with the diffusion features. We substantiate the efficacy of BDG in all-in-one image restoration, mixed degradation image restoration and real-world super-resolution.
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Ethics Statement. This paper presents work whose goal is to advance the field of image restoration. There are many potential societal consequences of our work. Given the increasing capabilities of image restoration techniques, forging misleading images or restoring and enhancing images for malicious purposes.
advocate avoiding the misuse of related technologies, such as
Reproducibility Statement. We state that BDG is highly reproducible. Implementation and datasets details and on our main experiences are provided in Section 4 and Appendix C. It is an- ticipated that these descriptions can sufficiently demonstrate the reproducibility of BDG. We plan to open-source the code and weight files after the paper passes peer review.
ACKNOWLEDGEMENT
This work was supported in part by National Key Research and Development Program of China 2025YFA18057000 in part by National Natural Science Foundation of China (82371112, 62501020); in part by the Science Foundation of Peking University Cancer Hospital (IC202505).
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A EXPERIMENTAL SETTINGS ON STUDIES ABOUT MAS-GLCM
TSNE settings in Sec. 3.1. In the TSNE visualization results, we select three weather-related degra- dations: haze, snow, and low-light, as well as two types of noise: Gaussian noise and Pepper noise. Degraded image data for hazy. low-light, and snowy conditions are obtained from SOTS, LOL,, and Snow 100K, respectively, while Gaussian and Pepper noise data are synthesized from the Kodak dataset with a Gaussian noise level of 25 and a Pepper noise ratio of 0.1. This experiment utilizes the TSNE function from "sklearn. "sklearn.manifold" to reduce the data dimensionality to two-dimensional space, with the number of iterations set to 2k and computation accelerated using four CPU cores.
Datasets used in Sec. 3.1. We select the first 100 images from SOTS, LOL. and Snow 100K as representative degraded image data for hazy, low-light, and snowy conditions, respectively, while the data with different noise types and noise levels are generated from the same Kodak dataset. Each image center-cropped to 256 x 256 to ensure a consistent resolution. We use the default setting of the KNeighborsClassifeer in the Python library sklearn, which assigns equal weights to all neigh- boring points and employs the Minkowski distance with the default parameter p=2, corresponding to the Euclidean distance.
OSTOO1 water
OSTO31 building low
Noise Level
high
OSTO01 water
OST031 building
low
Blur Level
high
Figure 5: MAS-GLCM on images with different texture.
More results MAS-GLCM. (1) MAS-GLCM robustness. We argue that MAS-GLCM in dif- ferent semantics is not significantly different, as shown in Figure 6. In the OST data set with seven different semantics, MAS-GLCM classifies the degradation well (77% in the noise level classifi- cation). 2) Real datasets. We evaluate MAS-GLCM on real datasets: low-light (LOLv2), snow (Snow100k), haze (RTTS) and noise (SIDD). The result can be shown in Figure 6 (c). 3) Scaling datasets. We also test MAS-GLCM on 5000 synthetic or real data, with the results shown in Fig- ure 4) Mixed datasets. We conduct experiments on the CDD dataset, which contains six compos- ite degradations: haze+rain, low+haze, low+rain, low+snow, tow-haze+rain, and low+haze+snow. T-SNE visualizations of the clustering behavior in Figure 7. These show that MAS-GI.CM success- fully separates certain categories such as low+snow and low+rain from others. However, it struggles to fully distinguish more similar composite degradations, such as low+haze+rain and low+haze.
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Hay Lowigg
LQ images. 49% Acc.
MAS-GLCM. 98% Acc.
(a) Synthetic data.
rery
Hay
barty
LQ images. 32% 32%.
MAS-GLCM Acc.
(b) woaldworld data.
Figure 6: More results on MAS-GLCM.
haze haze_rain
low haze
rai_rainn
low snow
low haze_raie
low haze snow
Figure 7: T-SNE results on mixed degradations.
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B
PROOF OF EQ. 4
The forward process of the diffusion model we use is as follows.
It = It-1 + afTres + B 6,41g1
(11)
where is the diffusing result in timestep i, Tres Blg Thg is the residual of the LQ image Tig and the HQ image Thg- Otz, Br, and ó is the noise coefficient of Wress standard Gaussian noise e, and Figs respectively.
Since we need to fit the distribution of Thgs we set zo = Thg. According to the Markov Chain, Eq. can be reformulated follows.
Tt = 3o + OrtTras + Bee 5,5t4g
(12)
where = -i Ci. 3r v Di B. and 0, Ei 8i.
Once this diffusion model is trained, we simulate the distribution p& ( through q( 1,,,, Flg z6: Tee)), where Ihe is the predicted residual and =g = 21g wres according to the definition of Tres.
Based on the Bayes' theorem, we can obtain the following.
Po + (0 11e, Fin: 10 FEe))
ạ( (( I8. 2 Vin
Tex))
( xd Fin)
2(
2t + 8p ayreee
(13)
exp
x
B
$7
18 + ar
1
Fin
x Clz 18
8 rest Ian))
B
As the goal of the formulation is to obtain the distribution of 2T-1, we simplify and rearrange it into a form about Jt-1 and C(z,, 18.= ales> Fin) is the term unrelated to it. So, the mean μe (Zx, f) and the variance σa o0 t) of the distribution p (zt-1 (xx) are follows.
8P E 8.
pe ( t) =x+ 1 agle
(14)
83 3/-11-1
(0(
De
C EXPERIMENTS SETUP AND QUALITATIVE COMPARISONS
C.1 ABLATION ON ANGLES AND SCALES IN MAS-GLCM
We evaluate several configurations with reduced or asymmetric angle and scale settings:
Incomplete angles. For instance, using only non-ne nog-negative angles (e.g., [0, 45, 90, 135, 180]) restricts the GLCM to capture co-occurrence patterns from directions above the current pixel, potentially missing symmetric opposing texture structures.
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Reduced angles. Using sparse set such as [-180, -90, 0. 90, 180] limits the model only horizontal and vertical relationships, ignoring diagonal textures that are common in natural images.
Limited scales. Reducing the number of distances decreases sensitivity to both fine-grained and coarse-level texture variations.
angles
scales
Avg. PSNR (dB) SSIM
[0, 45, 90, 135, 180]
[1,3, 5)
31.77/ / 0.932
[-180, -00, 0, 90, 180]
1- -,,,,,,55
31.93 / 0.944
1810 -33,- -90, -45, 0, 45, 90, 135, 180)
[-5. -3, 1,,1,,15,5
32.09 / 0.950
Table 8: Ablation angles and scales in MAS-GLCM.
As shown in Table 8, when the selected angles and scales provide comprehensive spatial coverage, restoration performance is consistently strong. The full configuration achieves the best results, in- dicating that complete directional and scale diversity helps the model better characterize complex degradation patterns. However, performance degrades noticeably when critical directions scales are omitted, particularly when symmetry or diagonal structures are neglected.
C.2 5D ALL-IN-ONE RESTORATION
Datasets. For all-in-one image restoration, datasets are: Rain13k (Yang al., 2017) and SynRain- 13k (Li et al., 2022b), which contains 13,712 training images for deraining; LOL (Wei et al., 2018), which contains 485 training images and 15 test images for low-light enhancement; Snow 100K Liu et al. (2018), which contains 50,000 training data, 50,000 testing data for desnowing; RESIDE (Li al., 2018), which contains 72,135 training images and 500 test images (SOTS) for dehazing: GoPro (Nah et al., 2017) and RealBlur for motion deblurring. Following Zheng et al. (2024), use the PSNR and SSIM calculated in the Y channel in the YCbCr space as metrics.
C.3 MIXED DEGRADTAION
Datasets. For the mixed degradation restoration task, we the CDD (Guo et al., 2025), which consists of 11 degradations (rain, low light, snow, and their and combinations). It has 13,013 image pairs for training and 2,200 for testing. Following Hu al. (2025a), we use the PSNR and SSIM calculated in the sRGB space as metrics.
Qualitative comparisons.
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Figure 8: Visual comparison on low-light haze rain.
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C.4 WORLD SUPER- RESOLUTION
Datasets. Following Wang et al. (2024); Wu et al. (2024), we use the LSDIR (Li et al., 2023) and the first 10k images of FFH (Karras al., 2019) for training data. Training pairs are synthesized via Real-ESRGAN (Wang et al.). For evaluation, we employ the following test sets: (1) We extract 3k randomly cropped 512 resolution patches from the DIV2K validation Agustsson & Timofte (2017), which are subsequently degraded using the same pipeline as used during training. This dataset is henceforth referred DIV2K- Val. (2) Additionally, we use center-cropped RealSR (Cai et a0 2019) and DRealSR (Wei et al., 2020) as real-world benchmarks, following Wang et al. (2024).
Metries. To offer a comprehensive assessment the performance of the various methods, we engage in spectrum of reference and non-reference metrics, PSNR and SSIM, computed in the Y channel the YCbCr color space, are used as reference-based fidelity metries, while LPIPS (Zhang et al., 2018) and DISTS (Ding et al., 2020) serve as reference- based perceptual quality metries. The FID statistic (Heusel et al., 2017) assesses the distributional divergence between the original and reconstructed images. In addition, MANIQA (Yang al., 2022), MUSIQ (Ke et al., 2021), and CLIPIQA (Wang et al., 2023a) are implemented as non-reference image quality metrics.
Qualitative comparisons.
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Figure 9: BDG handles complex noise and text details in images well, but still faces certain over- smoothing problems. Please zoom in for better view.
D LIMITATIONS AND FUTURE WORK
There remains considerable scope for advancing the design of MAS-GLCM and BDG. Specifically, the current implementation of MAS-GL.MM does not accommodate the detection of color deviations
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global geometric transformations, and it may exhibit sensitivity to image resolution. Moreover, BDG depends on relatively complex three-stage training paradigm. In future work, we intend to extend the generalization capability of MAS-GLCM to a broader range of low-level vision tasks and to further simplify and simplify the three-stage training strategy employed in BDG.
E THE USE OF LARGE LANGUAGE MODELS (LLMs)
LLMss are used to correct potential grammatical inaccuracies the manuscript. LLMs do not par- ticipate in research ideation.
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Reviews:
[{"review_id": "1Kxl1EhR4F", "reviewer": "Reviewer_wiEb", "summary": "The paper introduces BDG (Bridging Degradation Discrimination and Generation), a novel framework for universal image restoration that tackles the dual challenge of identifying diverse image degradations and generating high-quality restorations. To address this, BDG integrates a fine-grained degradation characterization method called Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM), which outperforms prior techniques in distinguishing degradation types and levels. BDG’s training is structured into three stages: generation (to capture rich textures via diffusion), bridging (to align MAS-GLCM features with diffusion features), and restoration (to fine-tune fidelity while preserving discriminative capacity). This alignment enables the model to adaptively restore images across varied degradation scenarios without altering the underlying architecture.", "questions": "1. The paper proposes “order classification” for real-world degradation modeling. I'm curious how effective this trick is and hope the authors can provide a ablation study or justification for it.\n2. The paper mentions \"full negative contrastive learning\" in the RFT stage without defined what are negative or positive samples.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. BDG introduces the Multi-Angle and multi-Scale Gray Level Co-occurrence Matrix (MAS-GLCM), which significantly improves the model’s ability to distinguish between various degradation types and levels. This enables more precise restoration tailored to the input image’s condition.\n2. By aligning MAS-GLCM features with diffusion model features during a dedicated bridging stage, BDG effectively combines degradation awareness with generative priors. This fusion allows the model to retain rich texture generation while improving fidelity.\n3. BDG achieves substantial performance gains without modifying the underlying network architecture. This makes it compatible with existing diffusion-based models and easy to adopt across diverse restoration tasks.", "weaknesses": "1. While MAS-GLCM is presented as a novel degradation discriminator, it is fundamentally an extension of the classical Gray Level Co-occurrence Matrix (GLCM), which has been widely used in texture analysis for decades. To strengthen the novelty claim, the authors should compare MAS-GLCM with frequency-aware representations and learned degradation embeddings (e.g., from PromptIR or DCPT) or  (e.g., Ji et al., 2021). Ablation studies showing MAS-GLCM’s superiority over these alternatives would help.\n2. The paper emphasizes fidelity (e.g., PSNR, SSIM) but does not provide sufficient perceptual quality metrics (e.g., LPIPS, NIQE, FID), especially for real-world super-resolution where perceptual realism is critical.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GOun6irvp4", "reviewer": "Reviewer_ieqX", "summary": "This paper proposes a new framework, BDG, for universal image restoration. It aims to bridge the gap between high-fidelity restoration and high-perceptual-quality generation, a key challenge in the field. The core idea is twofold: first, it introduces MAS-GLCM, a classical texture descriptor, to achieve fine-grained, content-agnostic degradation discrimination. Second, it proposes a three-stage diffusion model training strategy (generation, bridging, and restoration) to inject this discriminative information into a generative prior, balancing fidelity and texture generation. The method demonstrates strong quantitative results across all-in-one, mixed-degradation, and real-world SR tasks, notably improving fidelity (PSNR) in diffusion-based SR .", "questions": "**Questions**\n1.  Given the critical failure when $\\mathcal{L}_{deg-cls}$ is removed, how do the authors envision this model handling a completely novel degradation type not seen during training? Does the 'full negative contrastive learning' ($\\mathcal{L}_{fcnl}$)  used in the RFT stage help mitigate this, and if so, why not use it in the bridging stage?\n\n2. Could the authors comment on the computational cost and training time of the three-stage training  versus a potential joint, end-to-end approach?\n\n3. Why was a classical, handcrafted feature (GLCM)  chosen over a learnable feature extractor for degradation discrimination, which could potentially be trained end-to-end with the alignment loss   alone?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper achieves impressive empirical results, particularly in reconciling high fidelity metrics (PSNR) with the strong perceptual quality of diffusion models in real-world super-resolution .", "weaknesses": "My primary concern lies with the methodological innovation. The core degradation descriptor, MAS-GLCM , is a classical, handcrafted feature descriptor. While its effectiveness in separating degradations is well-demonstrated (Fig. 1), this approach feels like a step back from end-to-end deep feature learning. The system relies on this external, non-learned feature extractor, which is then 'aligned' with the diffusion U-Net's features. This raises questions about whether a more integrated, end-to-end learned degradation representation would be more powerful and elegant.\n\nSecond, the proposed three-stage training pipeline  introduces significant complexity and computational overhead. The model requires a full 300k iterations split across two distinct fine-tuning stages (bridging and restoration) after the initial generation pre-training. It is unclear if this multi-stage approach is strictly necessary or if a more unified training scheme could achieve similar results. The ablation in Table 5 shows that both stages are needed for this design, but it doesn't justify the design's inherent complexity versus a simpler alternative.\n\nThird, the reliance on explicit degradation classification  or the 'order classification' 10101010) for supervising the MAS-GLCM encoder is a potential bottleneck for real-world generalization. This framing assumes that degradations can be categorized into discrete classes or sequential orders. It is questionable how this system would perform on truly unseen or novel real-world degradations that fall outside the training distribution of these discrete labels. The ablation study (Table 7) shows the model catastrophically fails without $\\mathcal{L}_{deg-cls}$ , which suggests the model is overly-reliant on this explicit supervision and may lack the robustness to generalize to unclassified degradation types.\n\nFinally, while the real-world SR results in Table 4 are strong on fidelity, the model was still trained on synthesized degradation pairs. The zero-shot results in Table 2 are promising, but the fundamental reliance on a classification loss trained on synthetic data types (haze, snow, etc.)  may limit its effectiveness on real-world images where degradations are far more complex and varied than the training classes.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "PwcCsbEEY4", "reviewer": "Reviewer_RoK7", "summary": "This paper proposes BDG (Bridging Degradation Discrimination and Generation) — a unified diffusion-based framework for universal image restoration. The core idea is to explicitly bridge degradation discrimination (what type and level of corruption an image has) with image generation priors (rich texture synthesis ability of diffusion models).", "questions": "See Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "+ The unified formulation connecting discrimination and generation is interesting. \n+ Achieving quantitative and qualitative improvements over recent SOTA (DiffUIR, DCPT).", "weaknesses": "1.  Though losses and stages are ablated, there is no comparison of MAS-GLCM vs. simpler features (e.g., gradients, frequency) in restoration quality.\n\n2.  The paper does not report details about computational cost (params / memory overhead / time) of MAS-GLCM.\n\n3.  The number of scales × angles used ($l$, $\\theta$) and preprocessing details are not discussed.\n\n4.  Questions about MAS-GLCM :\n\n    (1)  Although the authors claim MAS-GLCM is “minimally affected by image content,” GLCM is a gray-level co-occurrence matrix and inherently relies on statistics of gray-level pairs. If an image contains prominent structures, repetitive textures, or skewed illumination, its gray-level relations will directly shape the GLCM pattern. For example, under the same blur level, a highly textured image (e.g., forests, buildings) and a low-texture image (e.g., sky, walls) can yield markedly different MAS-GLCM responses, creating representation variance for the same degradation strength.\n\n    (2)  MAS-GLCM shows some clustering ability in t-SNE, but when facing complex, composite degradation distributions GLCM has no built-in disentanglement mechanism. It primarily captures shifts in overall texture statistics, making it hard to attribute the change to a specific degradation type and unable to precisely estimate the mixture proportions/levels of multiple degradations. Could this be a reason why the method is less advantageous in real-world scenarios?\n\n    (3)  In contrast, degradation tokens, prompt embeddings, or degradation-aware latent vectors extracted by deep models offer stronger context awareness and semantic separation, leading to more reliable identification of both degradation type and severity.\n\n    (4)  MAS-GLCM also appears sensitive to image resolution. Would applying the same degradation type to images of different resolutions alter this descriptor in ways that hurt its generalization ability?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "eayUmcK3h7", "reviewer": "Reviewer_JVFR", "summary": "The method proposed in this paper follows a 'generate-then-restore' paradigm. Initially, a generative approach is applied to obtain a high-quality output. To compensate for potential deviations from the original image introduced during the generation process, two additional steps are employed. The first step uses degradation information to guide the generative model toward restoration, biasing the output toward the content of the input low-quality image; this degradation information is extracted using traditional operators, such as the gray-level co-occurrence matrix. The second step directly incorporates the low-quality image itself, further ensuring that the result follows the original content of the image.", "questions": "Please explain why there are not more visual results, and analyze the performance of the algorithm in real-world and diverse scenarios.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper integrates fine-grained degradation information (MAS-GLCM features) with the diffusion generative prior, achieving both high-fidelity restoration and rich texture generation. Compared to traditional full-reference methods or purely generative approaches, it effectively balances fidelity and perceptual quality.", "weaknesses": "1. The paper provides only six visual results, which is insufficient. Although some quantitative improvements are reported, improvements in low-level metrics do not necessarily correspond to better perceptual quality. Therefore, the current results are not sufficient to fully demonstrate the effectiveness of the proposed method; more visual examples are needed.\n\n2. MAS-GLCM is a hand-crafted feature, which may perform poorly on certain complex or previously unseen types of degradation. It may also be limited for non-grayscale or non-texture-related degradations, such as color shifts or geometric distortions.\n\n3. The training procedure is relatively complex, requiring a total of three stages.\n\n4. The three-stage training strategy combined with multiple loss functions adds additional complexity and requires careful hyperparameter tuning (e.g., the λ coefficient).\n\n5. Although the bridging and restoration fine-tuning stages improve fidelity, the generated content may still slightly deviate from the original image under extreme or composite degradation scenarios. Moreover, the performance on extreme or previously unseen real-world degradations remains unclear.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)