Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance

Anbin Xiong; Chunxiao Liu; Erli Meng; Wei Liu

REVIEW 4 major objections 5 minor 82 references

Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance

T0 review · 4 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read The paper shows that all-in-one image restoration improves when guidance is per-pixel rather than global, and reports a 1.51 dB PSNR gain over the previous best on composite degradations.

desk verdict Large reported gains on composite degradation benchmarks, but the pixel-level guidance mechanism is not actually demonstrated; the paper overclaims consistency. read the letter →

arxiv 2608.09482 v1 pith:NXT6B62D submitted 2026-08-10 cs.CV cs.AI

Chunxiao Liu , Wei Liu , Anbin Xiong , Erli Meng This is my paper · ORCID

classification cs.CVcs.AI

keywords all-in-oneimagerestorationpixel-levelguidancevisualprompttextualdegradationmapcompositedenoisinglow-lightenhancement

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that all-in-one image restoration models should not treat an image uniformly, because real degradations are spatially uneven: rain hits streaks, haze varies by depth, and noise may be localized. It proposes MGN-AIR, which predicts a per-pixel degradation map, combines it with a text description of the degradation type, and uses the resulting restoration matrix to choose a different restoration operation at each pixel. On the CDD11 benchmark, where several degradations co-occur, the method reports a 1.51 dB PSNR improvement over the nearest recent approach, and it also leads on a five-task all-in-one benchmark. The largest gains appear exactly where the pixel-level idea should help: images mixing multiple degradation types.

What carries the argument

The load-bearing object is the pixel-level restoration matrix $P\in[0,1]^{H\times W\times C}$, produced by the Multimodal Guidance Module from the visual prompt $V$, a per-pixel degradation severity map, and a global textual prompt. $P$ gates two restoration paths in the Pixel-Level Restoration Module: heavily degraded pixels are restored with standard and dilated convolutions, while lightly degraded pixels go through spatial attention over an $8\times 8$ window. The visual prompt itself is trained against a prescribed degradation map, Eq. (4), given by the min-max normalized per-pixel absolute difference between the clean and degraded images averaged across channels.

What would settle it

Take clean images, add no degradation, and compute the Eq. (4) map: if the map is not nearly uniform and close to zero but instead highlights edges or bright textures, then the supervision is measuring content rather than degradation. Alternatively, add uniform noise to a flat region and leave a high-texture region clean; if the model assigns higher severity to the clean textured region than to the noisy flat region, the pixel-level guidance is miscalibrated.

Watch

Extended reading notes

Core claim

MGN-AIR's central claim is that degradation-adaptive all-in-one restoration should be controlled at pixel resolution, not image level. The network's Visual Prompt Generation Module predicts a per-pixel degradation map, supervised at the first encoder level by the min-max normalized per-pixel absolute difference between clean and degraded images averaged over channels. A Multimodal Guidance Module fuses this map with a frozen text encoder's global degradation-type embedding to produce a restoration matrix $P\in[0,1]^{H\times W\times C}$. A Pixel-Level Restoration Module uses $P$ to blend a local convolution path, for heavily degraded pixels where only neighbors can be trusted, with a spatial-attention path, for lightly degraded pixels where recurring structural patterns elsewhere help. The paper reports consistent gains over prior all-in-one methods across three-, five-, and eleven-task benchmarks, with the largest margin on the composited CDD11 set.

Load-bearing premise

The method assumes that the min-max normalized per-pixel absolute difference between clean and degraded images is a faithful measure of degradation severity at each pixel, so strong edges, textures, or illumination differences in image content could mislead the visual prompt.

Editorial extensions

If this is right

A single MGN-AIR model handles denoising, deraining, deblurring, dehazing, desnowing, and low-light enhancement without task-specific adaptation.
On the CDD11 composite benchmark, the method reports an average PSNR of 30.56 dB, 1.51 dB above the nearest recent method.
Ablations show that removing the visual prompt lowers performance more than removing the textual prompt, so local intensity cues are the larger source of gain.
The restoration matrix makes per-pixel decisions explicit, so the model's choice of local versus global restoration can be inspected at each pixel.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The same pixel-level design could transfer to real-world restoration by replacing the supervised degradation map with an unsupervised severity estimate, since paired clean-degraded images are rarely available in practice.
The Eq. (4) supervision ties the visual prompt to any large pixel difference, so gains on synthetic benchmarks may shrink on real photographs where edges and illumination changes also produce large differences.
The predicted restoration matrix could serve as an editable control: users could raise or lower restoration strength in specific regions without retraining.
A natural extension is to supervise the visual prompt at deeper levels as well, which the current design only does implicitly.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Here's the short version: this paper could be an important data point for all-in-one restoration if the numbers hold, but the mechanism it credits for the gains is not supported by the evidence as written. Worth having a referee look at it, but the authors need to do real work on the attribution and the overclaims.

What's new: per-pixel visual prompt learned from the degraded image, combined with a CLIP text prompt to produce a restoration matrix that switches between local and global operations per pixel. That's a sensible step beyond global prompt-based methods like PromptIR and DA-CLIP. The CDD11 composite degradation results are remarkably strong, 1.51 dB over MoCE-IR on average, and the method also does well on the five-degradation and three-degradation benchmarks.

The architecture is clean and the ablations are the right shape: removing the visual prompt hurts, removing the text prompt hurts, and the lightweight variant still outperforms baselines.

Now the soft spots, in proportion.

The central attribution problem is real. The visual prompt is supervised by Eq. (4), the channel-averaged, min-max normalized absolute difference between clean and degraded images. That quantity is not a degradation map. For Gaussian noise it is a random field; for low-light it is dominated by clean-image intensity; for haze it is content-dependent. The VPGM cannot reliably predict it from the degraded image alone, so the claimed per-pixel degradation signal may be largely content-correlated or near-constant. The big ablation drop when the visual prompt is removed is suggestive, but it does not isolate the prompt: it also removes the auxiliary loss, the FFT-attention module, and some parameters. The paper reports no metric for how well the visual prompt matches the actual degradation, so the mechanism is not demonstrated.

The "consistently and significantly outperforms" claim is contradicted by the paper's own Table 2: denoising 31.12 dB vs DFPIR's 31.29 and MoCE-IR's 31.34, and low-light 23.67 vs DFPIR's 23.82. The average is better, but consistency is false.

There are also presentation issues: DA-CLIP is mentioned in the text but missing from Table 2, there are citation numbering errors, and no code or training details are released. Also, the paper never says how the text prompt is chosen at inference; if it needs the ground-truth degradation type, that should be explicit.

To be fair, the circularity concern is low: the final restoration is not derived from the map by a fitted derivation, the map is just an auxiliary supervision. That part I don't see as a problem.

Who this is for: anyone working on prompt-based all-in-one restoration, especially for mixed degradations. They should read it, but with care.

My recommendation: do not desk-reject. Send it to peer review, but the reviewers should require the authors to show that the visual prompt actually tracks degradation severity, to clarify the text-prompt setting, and to correct the overclaims. If the results are real, this is a useful paper after revision.

Referee Report

4 major / 5 minor

Summary. The paper proposes MGN-AIR, an all-in-one image restoration network that performs restoration at the pixel level by combining a visual prompt, estimated by a Visual Prompt Generation Module (VPGM), with a CLIP-based textual prompt in a Multimodal Guidance Module (MGM). The resulting restoration matrix is used in a Pixel-Level Restoration Module (PLRM) to blend local/dilated convolutions with spatial attention per pixel. The method is evaluated on the CDD11 composite-degradation benchmark and on standard five- and three-degradation all-in-one settings, reporting average PSNR/SSIM improvements over prior methods, including a 1.51 dB average PSNR gain over MoCE-IR on CDD11, together with ablations and a complexity comparison.

Significance. If the pixel-level guidance mechanism performs as claimed, the work would be a meaningful advance over global prompt-based all-in-one restoration methods, and the reported CDD11 improvement is substantial. Strengths of the manuscript include the breadth of the evaluation, the explicit auxiliary supervision of the visual prompt, and the complexity-control experiments in Table 5. However, the central mechanistic claim is not yet sufficiently supported: the supervision target in Eq. (4) conflates image content with degradation severity, the ablation in Table 4 is confounded, and the own tables contradict the phrase 'consistently outperforms' on the denoising task. These issues are load-bearing for the main contribution, so a major revision is required.

major comments (4)

[§3.2, Eq. (4)] The supervision target for the visual prompt is the channel-averaged, min-max normalized absolute difference between the clean image and the degraded image. This quantity does not faithfully represent per-pixel degradation severity: for additive Gaussian noise it is a realization of the noise field and is not identifiable from the degraded image alone, while for low-light and haze it is dominated by the clean image's local intensity and edges. The paper provides no evaluation of the predicted visual prompt itself (e.g., correlation with actual degradation severity), so the claimed pixel-level guidance mechanism is not established. Please add such an evaluation or adopt a more physically motivated supervision target.
[§4.2, Table 4] The ablation 'w/o visual prompt' removes the VPGM, the auxiliary loss L_Aux, the FFT-attention branch, and the associated parameters together, so the observed performance drop cannot be attributed specifically to pixel-level guidance. A control experiment with matched capacity and auxiliary supervision but with a content-agnostic or global prompt is needed, together with a quantitative accuracy metric for the predicted visual prompt, before the 0.73 dB gap over 'w/o MGB' can be assigned to the proposed mechanism.
[§4.2, Tables 2 and 3] The abstract's claim that the method 'consistently and significantly outperforms' existing approaches is contradicted by the paper's own denoising numbers: on BSD68 at sigma=25, MGN-AIR reports 31.12 dB versus DFPIR's 31.29 dB in Table 2, and the averaged denoising PSNR in Table 3 is 31.00 dB versus DFPIR's 31.29 dB and PromptIR's 31.12 dB. The claim should be qualified to average performance or accompanied by an explanation of this task-specific trade-off.
[§4.1, §4.2, Table 2] The comparison protocol is ambiguous: DA-CLIP is listed as a compared method in the text but is absent from Table 2, and the paper does not state whether the baseline numbers were obtained by retraining under the same data, patch sizes, and optimizer settings or were taken from the original papers. Without this information, the state-of-the-art claim on the five-degradation benchmark is not fully verifiable.

minor comments (5)

[§4.2 and Fig. 3 caption] The text refers to 'MoCE-IR [56]' in the CDD11 comparison and in the Fig. 3 caption, but reference [56] is DFPIR; the correct citation for MoCE-IR is [70].
[§3.2, Eq. (3)] The notation '·' in Eq. (3) is ambiguous; if it denotes elementwise multiplication in the Fourier domain, the operation implements circular convolution rather than the dot-product attention described in the text. Please clarify the exact operation and how the Softmax is applied.
[§3.3, Eq. (5)] The dimensions of T_p and the role of the scalar beta in 'Concat[Avg(T); beta]' are unclear; please specify how the 1D convolution transforms the concatenated vector into a spatial feature.
[§4.2, Table 5] The text says 'Ours-s' keeps a similar parameter count to PromptIR, but it has 26.61M versus 32.97M parameters while using more FLOPs (133.42 versus 121.08 G) and higher latency (35.84 versus 31.09 ms); the claim should be phrased more carefully.
[General] The paper does not release code or trained models; for a method whose main contribution is a new mechanism, providing code or at least predicted visual prompt maps would substantially strengthen reproducibility and help verify the mechanism.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the restoration output is a learned function, not a closed-form consequence of the Eq. (4) degradation map.

full rationale

The paper's derivation chain is empirical rather than deductive: the visual prompt is supervised by the degradation map in Eq. (4), but the final restoration is produced by learned modules (VPGM, MGM, PLRM) optimized with the total loss in Eq. (7), not algebraically derived from that map. The degradation map is a training target, not a fitted parameter substituted into a closed-form prediction. The reported gains are benchmark comparisons against standard methods trained on the same or similar data, which is external evidence, and the paper contains no load-bearing self-citations or imported uniqueness arguments. The concern that Eq. (4) conflates image content with degradation severity is a correctness or attribution issue, not circularity, because no equation reduces the final output to the visual prompt by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The central claim rests on supervised learning over synthetic benchmarks. The paper introduces no new physical entities; the visual prompt and restoration matrix are learned internal representations. The main free parameters are hyperparameters and loss weights that are not specified in the paper.

free parameters (4)

Loss weights for L_SSIM, L_Percep, L_Aux = not specified
The total loss in Eq. (7) sums four terms without stating relative weights; these weights affect optimization and final performance.
Window size r = 32
Window size for FFT self-attention in VPGM is set to 32 by hand.
Channel dimension C in MGM = 32
The fused textual feature dimension in MGM is set to 32.
Transformer blocks per level = [4, 6, 6, 8]
Architecture depth is taken from PromptIR/DFPIR, not derived.

assumptions (4)

domain assumption The min-max normalized per-pixel absolute difference between clean and degraded images is a faithful degradation-intensity map (Eq. 4).
This supervises the visual prompt; if content structure dominates the difference, the prompt is miscalibrated.
domain assumption Frozen CLIP text embeddings provide useful global cues for distinguishing degradation types in all-in-one restoration.
Used in MGM; this is a transfer-learning assumption grounded in prior work.
domain assumption The U-Net encoder-decoder with four levels is a sufficient backbone for all-in-one restoration.
Borrowed from PromptIR and DFPIR; not derived in this paper.
domain assumption The standard benchmarks (CDD11, SOTS, Rain100L, BSD68, GoPro, LOLv1) are valid proxies for restoration quality.
Evaluation relies on PSNR/SSIM on these synthetic benchmarks.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance." pith.science (2026). https://pith.science/paper/NXT6B62D

@misc{pith2026260809482,
  author       = {Pith},
  title        = {Pith review of: Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/NXT6B62D}},
  note         = {Machine review of arXiv:2608.09482}
}

read the original abstract

All-in-one image restoration is a unified low-level vision task that aims to effectively recover high-quality images from inputs degraded by various types and levels of corruption using a single model. Recent works have achieved remarkable progress by learning degradation-adaptive prompts or network architectures. However, these methods typically apply a uniform restoration strategy across the entire image, neglecting the fact that different regions may suffer from distinct degradation types and varying degrees of severity. In contrast, we propose to perform restoration at the pixel level, thereby enabling more fine-grained and precise control over the restoration process. Specifically, we present MGN-AIR, a novel pixel-level restoration framework for all-in-one image restoration. Our approach first learns to estimate a pixel-level visual prompt. Then, it leverages both textual and visual prompts to provide global and local degradation cues, guiding the model on where to look and how to restore at each pixel. We conduct extensive experiments on multiple all-in-one image restoration benchmarks, covering a wide range of tasks including denoising, deraining, deblurring, dehazing, desnowing, and low-light enhancement. Experimental results demonstrate that our proposed method consistently and significantly outperforms existing approaches.

Figures

Figures reproduced from arXiv: 2608.09482 by the authors.

**Figure 2.** Overall framework of our MGN-AIR. The left part is the U-net architecture, which consists of our proposed multimodal [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗

**Figure 3.** Visual results of MoCE-IR [56], OneRestore [17] and our approach on CDD11 dataset [17]. We provide the global image and local detail grounded by different colors for better visualization. MGN-AIR effectively removes haze, rain and snow streaks while preserving local details in the image. of our approach also outperforms existing methods. The performance on denoising task is slightly lower than state-of-the-arts, wh… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

82 extracted references · 61 canonical work pages

[1]

Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. 2010. Con- tour detection and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence33, 5 (2010), 898–916

2010
[2]

Chenghao Chen and Hao Li. 2021. Robust representation learning with feedback for single image deraining. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 7742–7751

2021
[3]

Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. 2022. Simple baselines for image restoration. InEuropean conference on computer vision. Springer, 17–33

2022
[4]

Lufei Chen, Xiangpeng Tian, Shuhua Xiong, Yinjie Lei, and Chao Ren. 2024. Unsupervised blind image deblurring based on self-enhancement. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 25691– 25700

work page 2024
[5]

Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, and Sy-Yen Kuo. 2020. PMHLD: Patch map-based hybrid learning DehazeNet for single image haze removal.IEEE Transactions on Image Processing29 (2020), 6773–6788

work page 2020
[6]

Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xin Yuan, et al. 2022. Cross aggregation transformer for image restoration.Advances in Neural Information Processing Systems35 (2022), 25478–25490

work page 2022
[7]

Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko. 2021. Rethinking coarse-to-fine approach in single image deblurring. InProceedings of the IEEE/CVF international conference on computer vision. 4641–4650

work page 2021
[8]

Marcos V Conde, Gregor Geigle, and Radu Timofte. 2024. Instructir: High-quality image restoration following human instructions. InEuropean Conference on Computer Vision. Springer, 1–21

work page 2024

Show all 82 references

[9]

Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, and Fahad Shahbaz Khan. 2025. Adair: Adaptive all-in-one image restoration via frequency mining and modulation. In13th International Conference on Learning Representations, ICLR 2025. International Conferenc...

2025
[10]

Sourya Dipta Das and Saikat Dutta. 2020. Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 482–483

2020
[11]

Yu Dong, Yihao Liu, He Zhang, Shifeng Chen, and Yu Qiao. 2020. FD-GAN: Generative adversarial networks with fusion-discriminator for single image dehazing. InProceedings of the AAAI conference on artificial intelligence, Vol. 34. 10729–10736

2020
[12]

Huiyu Duan, Xiongkuo Min, Sijing Wu, Wei Shen, and Guangtao Zhai. 2024. Uniprocessor: a text-induced unified low-level image processor. InEuropean Conference on Computer Vision. Springer, 180–199

2024
[13]

Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, and Baoquan Chen. 2019. A general decoupled learning framework for parameterized image operators.IEEE transactions on pattern analysis and machine intelligence43, 1 (2019), 33–47

2019
[14]

Hongyun Gao, Xin Tao, Xiaoyong Shen, and Jiaya Jia. 2019. Dynamic scene deblurring with parameter selective sharing and nested skip connections. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 3848–3856

2019
[15]

Chunle Guo, Chongyi Li, Jichang Guo, Chen Change Loy, Junhui Hou, Sam Kwong, and Runmin Cong. 2020. Zero-reference deep curve estimation for low- light image enhancement. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1780–1789

2020
[16]

Hang Guo, Jinmin Li, Tao Dai, Zhihao Ouyang, Xudong Ren, and Shu-Tao Xia
[17]

Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, and Shengfeng He. 2024. Onerestore: A universal restoration framework for composite degradation. In European conference on computer vision. Springer, 255–272

2024
[18]

JiaKui Hu, Lujia Jin, Zhengjian Yao, and Yanye Lu. [n. d.]. Universal Image Restora- tion Pre-training via Degradation Classification. InThe Thirteenth International Conference on Learning Representations
[19]

Jie Huang, Xiao Liu, Yizhong Pan, Xiaohai He, and Chao Ren. 2022. CasaPuNet: channel affine self-attention-based progressively updated network for real image denoising.IEEE Transactions on Industrial Informatics19, 8 (2022), 9145–9156

2022
[20]

Jiachen Jiang, Tianyu Ding, Ke Zhang, Jinxin Zhou, Tianyi Chen, Ilya Zharkov, Zhihui Zhu, and Luming Liang. 2025. Cat-AIR: Content and Task-Aware All-in- One Image Restoration.arXiv preprint arXiv:2503.17915(2025)

2025 arXiv
[21]

Lingshun Kong, Jiangxin Dong, Jianjun Ge, Mingqiang Li, and Jinshan Pan
[22]

Orest Kupyn, Volodymyr Budzan, Mykola Mykhailych, Dmytro Mishkin, and Jiří Matas. 2018. Deblurgan: Blind motion deblurring using conditional adversarial networks. InProceedings of the IEEE conference on computer vision and pattern recognition. 8183–8192

2018
[23]

Orest Kupyn, Tetiana Martyniuk, Junru Wu, and Zhangyang Wang. 2019. Deblurgan-v2: Deblurring (orders-of-magnitude) faster and better. InProceedings of the IEEE/CVF international conference on computer vision. 8878–8887

2019
[24]

Jaakko Lehtinen, Jacob Munkberg, Jon Hasselgren, Samuli Laine, Tero Karras, Miika Aittala, and Timo Aila. 2018. Noise2Noise: Learning image restoration without clean data.arXiv preprint arXiv:1803.04189(2018)

2018 arXiv
[25]

Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, and Xi Peng. 2022. All- in-one image restoration for unknown corruption. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 17452–17462

2022
[26]

Boyi Li, Wenqi Ren, Dengpan Fu, Dacheng Tao, Dan Feng, Wenjun Zeng, and Zhangyang Wang. 2018. Benchmarking single-image dehazing and beyond.IEEE transactions on image processing28, 1 (2018), 492–505

2018
[27]

Ruoteng Li, Loong-Fah Cheong, and Robby T Tan. 2019. Heavy rain image restoration: Integrating physics model and conditional adversarial learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1633–1642

2019
[28]

Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, and Hongbin Zha. 2018. Recurrent squeeze-and-excitation context aggregation net for single image deraining. In Proceedings of the European conference on computer vision (ECCV). 254–269

2018
[29]

Jingyun Liang, Jiezhang Cao, Guolei Sun, Kai Zhang, Luc Van Gool, and Radu Timofte. 2021. Swinir: Image restoration using swin transformer. InProceedings of the IEEE/CVF international conference on computer vision. 1833–1844

2021
[30]

Rongxin Liao, Feng Li, Yanyan Wei, Zenglin Shi, Le Zhang, Huihui Bai, and Meng Wang. 2025. Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal.arXiv preprint arXiv:2503.09013(2025)

2025
[31]

Xin Lin, Chao Ren, Xiao Liu, Jie Huang, and Yinjie Lei. 2023. Unsupervised image denoising in real-world scenarios via self-collaboration parallel generative adversarial branches. InProceedings of the IEEE/CVF International Conference on Computer Vision. 12642–12652

2023
[32]

Xin Lin, Jingtong Yue, Sixian Ding, Chao Ren, Lu Qi, and Ming-Hsuan Yang. 2024. Dual degradation representation for joint deraining and low-light enhancement in the dark.IEEE transactions on circuits and systems for video technology(2024)

2024
[33]

Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, and Qi Tian. 2022. Tape: Task-agnostic prior embedding for image restoration. InEuropean conference on computer vision. Springer, 447–464

2022
[34]

Xiaohong Liu, Yongrui Ma, Zhihao Shi, and Jun Chen. 2019. Griddehazenet: Attention-based multi-scale network for image dehazing. InProceedings of the IEEE/CVF international conference on computer vision. 7314–7323

2019
[35]

Ze Liu, Yutong Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. 2021. Swin transformer: Hierarchical vision transformer us- ing shifted windows. InProceedings of the IEEE/CVF international conference on computer vision. 10012–10022

2021
[36]

Wenyang Luo, Haina Qin, Zewen Chen, Libin Wang, Dandan Zheng, Yuming Li, Yufan Liu, Bing Li, and Weiming Hu. 2025. Visual-Instructed Degradation Diffusion for All-in-One Image Restoration. InProceedings of the Computer Vision and Pattern Recognition Conference. 12764–12777

2025
[37]

Ziwei Luo, Fredrik K Gustafsson, Zheng Zhao, Jens Sjölund, and Thomas B Schön
[38]

Kede Ma, Zhengfang Duanmu, Qingbo Wu, Zhou Wang, Hongwei Yong, Hongliang Li, and Lei Zhang. 2016. Waterloo exploration database: New chal- lenges for image quality assessment models.IEEE Transactions on Image Processing 26, 2 (2016), 1004–1016

2016
[39]

Long Ma, Tengyu Ma, Risheng Liu, Xin Fan, and Zhongxuan Luo. 2022. Toward fast, flexible, and robust low-light image enhancement. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5637–5646

2022
[40]

Controlling Vision-Language Models for Multi-Task Image Restoration. In ICLR
[41]

Chong Mou, Qian Wang, and Jian Zhang. 2022. Deep generalized unfolding networks for image restoration. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 17399–17410

2022
[42]

Seungjun Nah, Tae Hyun Kim, and Kyoung Mu Lee. 2017. Deep multi-scale convolutional neural network for dynamic scene deblurring. InProceedings of the IEEE conference on computer vision and pattern recognition. 3883–3891

2017
[43]

David Martin, Charless Fowlkes, Doron Tal, and Jitendra Malik. 2001. A database of human segmented natural images and its application to evaluating segmenta- tion algorithms and measuring ecological statistics. InProceedings eighth IEEE international conference on computer vis...

2001
[44]

Yizhong Pan, Xiao Liu, Xiangyu Liao, Yuanzhouhan Cao, and Chao Ren. 2023. Random sub-samples generation for self-supervised real image denoising. In Proceedings of the IEEE/CVF international conference on computer vision. 12150– 12159

2023
[45]

Yizhong Pan, Chao Ren, Xiaohong Wu, Jie Huang, and Xiaohai He. 2022. Real image denoising via guided residual estimation and noise correction.IEEE Transactions on Circuits and Systems for Video Technology33, 4 (2022), 1994–2000

2022
[46]

Ozan Özdenizci and Robert Legenstein. 2023. Restoring vision in adverse weather conditions with patch-based denoising diffusion models.IEEE transactions on pattern analysis and machine intelligence45, 8 (2023), 10346–10357

2023
[47]

Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, and Fahad Khan. 2023. PromptIR: Prompting for All-in-One Image Restoration. InThirty-seventh Con- ference on Neural Information Processing Systems

2023
[48]

Xu Qin, Zhilin Wang, Yuanchao Bai, Xiaodong Xie, and Huizhu Jia. 2020. FFA-Net: Feature fusion attention network for single image dehazing. InProceedings of the AAAI conference on artificial intelligence, Vol. 34. 11908–11915

2020
[49]

Dongwon Park, Dong Un Kang, Jisoo Kim, and Se Young Chun. 2020. Multi- temporal recurrent neural networks for progressive non-uniform single image deblurring with incremental temporal training. InEuropean conference on com- puter vision. Springer, 327–343. Beyond Uniform Resto...

2020
[50]

Chao Ren, Xiaohai He, Chuncheng Wang, and Zhibo Zhao. 2021. Adaptive consistency prior based deep network for image denoising. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 8596–8606

2021
[51]

Chao Ren, Yizhong Pan, and Jie Huang. 2022. Enhanced latent space blind model for real image denoising via alternative optimization.Advances in Neural Information Processing Systems35 (2022), 38386–38399

2022
[52]

Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. 2021. Learning transferable visual models from natural language supervision. InInternational conference on machine learnin...

2021
[53]

Yuanjie Shao, Lerenhan Li, Wenqi Ren, Changxin Gao, and Nong Sang. 2020. Domain adaptation for image dehazing. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2808–2817

2020
[54]

Karen Simonyan and Andrew Zisserman. 2014. Very deep convolutional networks for large-scale image recognition.arXiv preprint arXiv:1409.1556(2014)

2014 arXiv
[55]

Olaf Ronneberger, Philipp Fischer, and Thomas Brox. 2015. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention. Springer, 234–241

2015
[56]

Xiangpeng Tian, Xiangyu Liao, Xiao Liu, Meng Li, and Chao Ren. 2025. Degradation-Aware Feature Perturbation for All-in-One Image Restoration. In Proceedings of the Computer Vision and Pattern Recognition Conference. 28165– 28175

2025
[57]

Jeya Maria Jose Valanarasu, Rajeev Yasarla, and Vishal M Patel. 2022. Tran- sweather: Transformer-based restoration of images degraded by adverse weather conditions. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2353–2363

2022
[58]

Ying Tai, Jian Yang, Xiaoming Liu, and Chunyan Xu. 2017. Memnet: A persistent memory network for image restoration. InProceedings of the IEEE international conference on computer vision. 4539–4547

2017
[59]

Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, and Houqiang Li. 2022. Uformer: A general u-shaped transformer for image restoration. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 17683–17693

2022
[60]

Chen Wei, Wenjing Wang, Wenhan Yang, and Jiaying Liu. 2018. Deep Retinex Decomposition for Low-Light Enhancement. (2018)

2018
[61]

Guoqing Wang, Changming Sun, and Arcot Sowmya. 2019. Erl-net: Entangled representation learning for single image de-raining. InProceedings of the IEEE/CVF International Conference on Computer Vision. 5644–5652

2019
[62]

Gang Wu, Junjun Jiang, Yijun Wang, Kui Jiang, and Xianming Liu. 2025. Debiased all-in-one image restoration with task uncertainty regularization. InProceedings of the AAAI Conference on Artificial Intelligence, Vol. 39. 8386–8394

2025
[63]

Haiyan Wu, Yanyun Qu, Shaohui Lin, Jian Zhou, Ruizhi Qiao, Zhizhong Zhang, Yuan Xie, and Lizhuang Ma. 2021. Contrastive learning for compact single image dehazing. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10551–10560

2021
[64]

Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, and Liqiang Nie. 2025. Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration.arXiv preprint arXiv:2504.09973(2025)

2025
[65]

Qiuhai Yan, Aiwen Jiang, Kang Chen, Long Peng, Qiaosi Yi, and Chunjie Zhang
[66]

Fuzhi Yang, Huan Yang, Jianlong Fu, Hongtao Lu, and Baining Guo. 2020. Learning texture transformer network for image super-resolution. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5791–5800

2020
[67]

Wenhui Wu, Jian Weng, Pingping Zhang, Xu Wang, Wenhan Yang, and Jianmin Jiang. 2022. Uretinex-net: Retinex-based deep unfolding network for low-light image enhancement. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5901–5910

2022
[68]

Zhiwen Yang, Haowei Chen, Ziniu Qian, Yang Yi, Hui Zhang, Dan Zhao, Bingzheng Wei, and Yan Xu. 2024. All-in-one medical image restoration via task-adaptive routing. InInternational Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 67–77

2024
[69]

Xiaoyan Yu, Shen Zhou, Huafeng Li, and Liehuang Zhu. 2024. Multi-expert adap- tive selection: Task-balancing for all-in-one image restoration.IEEE Transactions on Circuits and Systems for Video Technology(2024)

2024
[70]

Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, and Radu Timofte. 2025. Complexity experts are task-discriminative learners for any image restoration. InProceedings of the Computer Vision and Pattern Recognition Conference. 12753–12763

2025
[71]

Hao Yang, Liyuan Pan, Yan Yang, and Wei Liang. 2024. Language-driven all- in-one adverse weather removal. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 24902–24912

2024
[72]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, Ming-Hsuan Yang, and Ling Shao. 2021. Multi-stage progressive image restoration. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 14821–14831

2021
[73]

Haijin Zeng, Xiangming Wang, Yongyong Chen, Jingyong Su, and Jie Liu. 2025. Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks. InProceedings of the Computer Vision and Pattern Recognition Conference. 7524– 7533

2025
[74]

Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, and Feng Zhao. 2023. Ingredient-oriented multi-degradation learning for image restoration. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5825–5835

2023
[75]

Syed Waqas Zamir, Aditya Arora, Salman Khan, Munawar Hayat, Fahad Shah- baz Khan, and Ming-Hsuan Yang. 2022. Restormer: Efficient transformer for high-resolution image restoration. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5728–5739

2022
[76]

Kai Zhang, Wangmeng Zuo, Shuhang Gu, and Lei Zhang. 2017. Learning deep CNN denoiser prior for image restoration. InProceedings of the IEEE conference on computer vision and pattern recognition. 3929–3938

2017
[77]

Yulun Zhang, Kunpeng Li, Kai Li, Bineng Zhong, and Yun Fu. 2019. Residual non- local attention networks for image restoration.arXiv preprint arXiv:1903.10082 (2019)

2019 arXiv
[78]

Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, and Xiaowei Hu. 2023. Learning weather-general and weather-specific features for image restoration under multiple adverse weather conditions. In Proceedings of the IEEE/CVF conference on computer vi...

2023
[79]

Kaihao Zhang, Wenhan Luo, Yiran Zhong, Lin Ma, Bjorn Stenger, Wei Liu, and Hongdong Li. 2020. Deblurring by realistic blurring. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2737–2746

2020
[2023]

InProceedings of the IEEE/CVF conference on computer vision and pattern recognition

Efficient frequency domain-based transformers for high-quality image deblurring. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 5886–5895
[2024]

InEuropean conference on computer vision

Mambair: A simple baseline for image restoration with state-space model. InEuropean conference on computer vision. Springer, 222–241
[2025]

Textual prompt guided image restoration.Engineering Applications of Artificial Intelligence155 (2025), 110981

2025

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.

[1] [1]

Pablo Arbelaez, Michael Maire, Charless Fowlkes, and Jitendra Malik. 2010. Con- tour detection and hierarchical image segmentation.IEEE transactions on pattern analysis and machine intelligence33, 5 (2010), 898–916

2010

[2] [2]

Chenghao Chen and Hao Li. 2021. Robust representation learning with feedback for single image deraining. InProceedings of the IEEE/CVF conference on computer vision and pattern recognition. 7742–7751

2021

[3] [3]

Liangyu Chen, Xiaojie Chu, Xiangyu Zhang, and Jian Sun. 2022. Simple baselines for image restoration. InEuropean conference on computer vision. Springer, 17–33

2022

[4] [4]

Lufei Chen, Xiangpeng Tian, Shuhua Xiong, Yinjie Lei, and Chao Ren. 2024. Unsupervised blind image deblurring based on self-enhancement. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 25691– 25700

work page 2024

[5] [5]

Wei-Ting Chen, Hao-Yu Fang, Jian-Jiun Ding, and Sy-Yen Kuo. 2020. PMHLD: Patch map-based hybrid learning DehazeNet for single image haze removal.IEEE Transactions on Image Processing29 (2020), 6773–6788

work page 2020

[6] [6]

Zheng Chen, Yulun Zhang, Jinjin Gu, Linghe Kong, Xin Yuan, et al. 2022. Cross aggregation transformer for image restoration.Advances in Neural Information Processing Systems35 (2022), 25478–25490

work page 2022

[7] [7]

Sung-Jin Cho, Seo-Won Ji, Jun-Pyo Hong, Seung-Won Jung, and Sung-Jea Ko. 2021. Rethinking coarse-to-fine approach in single image deblurring. InProceedings of the IEEE/CVF international conference on computer vision. 4641–4650

work page 2021

[8] [8]

Marcos V Conde, Gregor Geigle, and Radu Timofte. 2024. Instructir: High-quality image restoration following human instructions. InEuropean Conference on Computer Vision. Springer, 1–21

work page 2024