Junjun Jiang, Zengyuan Zuo,Gang Wu, Kui Jiang and Xianming Liu
AIIA Lab, Harbin Institute of Technology.
-
Awesome-AiOIR-Methods
2020 | 2021 | 2022 | 2023 | 2024 | 2025 | 2026 -
Awesome-AiOIR-Benchmarks
Three Challenging Datasets | Five Challenging Datasets | All-Weather | Real-World Weather-Related | CDD-11 | FoundIR's Datasets
- ✅ Our comprehensive survey paper on All-in-One Image Restoration has been released and is actively maintained.
📝 Submitted on 19 Oct 2024 (v1), last revised on 12 Jun 2025 (v2) with major updates and reductions.
We will continue to reflect the latest advancements in this evolving field.
| Paper | Avenue | Link | Code |
|---|---|---|---|
| Beyond Uniform Restoration: Empowering All-in-One Restoration with Pixel-Level Multimodal Guidance Chunxiao Liu, Wei Liu, Anbin Xiong, Erli Meng |
ACM MM 2026 | Paper | |
| Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal Wanshu Fan, Yunzhe Zhang, Yue Shen, Liyan Wang, Jing Qin, Kin-Man Lam, Cong Wang, Jinshan Pan |
TIP 2026 | Paper | Code |
| Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration Zihao He, Yunfeng Wu, Xinchao Wang, Songhua Liu |
ICML 2026 | Paper | |
| SpikeRestormer: Towards Energy-Efficient All-in-One Image Restoration via Unified Event Reasoning Shengkai Hu, Jie Shao, Jiaqi Ma, Xu Zhang, Keying Wu, Qilu Zhu, Beihang Song, Jun Wan |
arXiv | Paper | |
| MoCRA: Mixture of Compositional Rank-1 Atoms for 4K All-in-One Video Restoration Yongcong Wang, Pu Wang, Hingchin Chen, Runci Bai, Yucheng Xin, Chen Wu, Chengchao Shen, Guangwei Gao, Siyuan Yao, Pengwen Dai, Zhuoran Zheng |
arXiv | Paper | |
| What to Remove, What to Preserve: Dual-Ambiguity Rectification for All-in-One Image Restoration Cencen Liu, Wen Yin, Dongyang Zhang, Dongmin Li, Shan Zhao, Bing Su, Tao He, Jielei Wang, Guoming Lu |
arXiv | Paper | |
| CoRE-UIR: Prior-guided Common and Residual Experts for Efficient All-in-One Remote Sensing Image Restoration Zaiyan Zhang, Qiangqiang Yuan, Jie Li, Ziyang Lihe, Yu Wan, Yuzeng Chen, Xin Su, Liangpei Zhang |
arXiv | Paper | Code |
| Causal-AgentIR: Self-Evolving Causal Memory for Adaptive Image Restoration Agents Hu Gao, Yulong Chen, Lizhuang Ma |
arXiv | Paper | |
| Restore-R1: Efficient Image Restoration Agents via Reinforcement Learning with Multimodal LLM Perceptual Feedback Jianglin Lu, Yuanwei Wu, Ziyi Zhao, Hongcheng Wang, Felix Jimenez, Abrar Majeedi, Yun Fu |
CVPR 2026 | ||
| IAMAgent: Toward an Interactive and Adaptive Multi-Agent System for Image Restoration Yanyan Wei, Yilin Zhang, Zhao Zhang, Huan Zheng, Jiahuan Ren, Xiaogang Xu, Meng Wang, Zenglin Shi |
TIP 2026 | Paper | |
| QuReC: All-in-One Image Restoration with Query-Specific Guidance and Local-Global Response Calibration Shen Zhou, Jinghui Zhang, Wenbo Huang, Xuwei Qian, Zhen Wu, Guangwen Peng, Zhiyuan Li, Ding Ding, Dian Shen, Fang Dong |
ACM MM 2026 | Paper | Code |
| Virtual Consistency Model for All-in-one Image Restoration Jiawei Wu, Luwei Tu, Zhe Wang, Zhi Jin, Kaihao Zhang, Wenqi Ren, Xiaochun Cao |
TIP 2026 | Paper | |
| Transformer-Inspired Convolutional Network for Image Restoration Yuning Cui, Mingyu Liu, Wenqi Ren, Boxin Shi, Alois Knoll |
TCSVT 2026 | Paper | |
| Prompt-In-Prompt Learning for Universal Image Restoration Zilong Li, Yiming Lei, Chenglong Ma, Junping Zhang, Hongming Shan |
PR 2026 | Paper | Code |
| Bend the Basics: Degradation-Aware Deformable Tokenization for All-in-One Image Restoration Zihao He, Yunfeng Wu, Xinchao Wang, Songhua Liu |
ICML 2026 | Paper | |
| Hierarchical Physical-Chain Decoupling With Geo-Semantic MoLoRA for All-in-One Multimodal Remote Sensing Image Restoration Zhentao Zou, Jichu Zhan, Yue Zhou, Ran Ding, Jia Fu, Chaofeng Chen, Jianxin Xing, Xiaogang Yu, Xue Jiang |
TGRS 2026 | Paper | Code |
| Learning Dual Transformers for All-in-One Image Restoration From a Frequency Perspective Jie Chu, Tong Su, Pei Liu, Yunpeng Wu, Le Zhang, Zenglin Shi, Meng Wang |
TNNLS 2026 | Paper | |
| Learning Adaptive Dynamical Features via Multi-τ Liquid-Mamba for All-in-one Image Restoration Hu Gao, Changshuo Wang, Yulong Chen, Lizhuang Ma |
arXiv | Paper | |
| DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration Yanjie Tu, Qingsen Yan, Axi Niu, Tao Hu, Haokui Zhang, Jiantao Zhou |
arXiv | Paper | Code |
| Universal Image Restoration via Internalized Chain-of-Thought Reasoning Yu Guo, Zhengru Fang, Shengfeng He, Senkang Hu, Yihang Tao, Phone Lin, Yuguang Fang |
arXiv | Paper | Code |
| DDTNet: Degradation Disentanglement and Transfer Network for Test-Time All-in-One De-weathering Adaptation Kuan-Hung Lin, Fu-Jen Tsai, Yan-Tsung Peng, Min-Hung Chen, Chia-Wen Lin, Yen-Yu Lin |
arXiv | Paper | |
| Hierarchical Physical-Chain Decoupling With Geo-Semantic MoLoRA for All-in-One Multimodal Remote Sensing Image Restoration Zhentao Zou, Jichu Zhan, Yue Zhou, Ran Ding, Jia Fu, Chaofeng Chen, Jianxin Xing, Xiaogang Yu, Xue Jiang |
TGRS 2026 | Paper | Code |
| Degradation-Consistent Test-Time Adaptation for All-in-One Image Restoration Ni Tang, Shenghao Nie, Xiaotong Luo, Yuan Xie, Yanyun Qu |
CVPR 2026 | Paper | |
| Retrieve-to-Restore: Efficient All-in-One Image Restoration with a Retrieval-Based Degradation Bank Chenxu Wang, Kai Zhang, Jian Yang |
CVPR 2026 | Paper | Code |
| DiTTo: Scalable Order-aware All-in-One Image Restoration Agent Seungho Choi, Jihyong Oh |
arXiv | Paper | Code |
| OPERA: An Agent for Image Restoration with End-to-End Joint Planning-Execution Optimization Feng Zhu, Shuyang Xie, Yihan Zeng, Ming Liu, Wangmeng Zuo |
arXiv | Paper | Code |
| Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration Xinghua Huang, Zhixiong Yang, Chen Wu, Shengxi Li, Shuaifeng Zhi, Yue Zhang, Qibin Hou, Xin Deng, Jingyuan Xia |
arXiv | Paper | |
| Expandable, Compressible, Mineable: Open-World Thermal Image Restoration Pu Li, Huafeng Li, Yafei Zhang, Wen Wang, Neng Dong, Jie Wen |
ICML 2026 | Paper | Code |
| Leveraging Multimodal Large Language Models for All-in-One Image Restoration via a Mixture of Frequency Experts Eunho Lee, Rei Kawakami, Youngbae Hwang |
arXiv | Paper | |
| DRNet: All-in-One Image Restoration via Prior-Guided Dynamic Reparameterization Ao Li, Xiaoning Liu, Sheng Li, Yapeng Du, Zhen Long, Lei Luo, Le Zhang, Ce Zhu |
TMM 2026 | Paper | Code |
| Continuous Expert Assembly: Instance-Conditioned Low-Rank Residuals for All-in-One Image Restoration Haisen He, Xiangyu Zou, SongLin Dong, Heng Li, Yihong Gong, Zhiheng Ma |
arXiv | Paper | |
| PVRF: All-in-one Adverse Weather Removal via Prior-modulated and Velocity-constrained Rectified Flow Wei Dong, Han Zhou, Terry Ji, Guanhua Zhao, Shahab Asoodeh, Yulun Zhang, Guangtao Zhai, Jun Chen, Xiaohong Liu |
arXiv | Paper | Code |
| UARE: A Unified Vision-Language Model for Image Quality Assessment, Restoration, and Enhancement Weiqi Li, Xuanyu Zhang, Bin Chen, Jingfen Xie, Yan Wang, Kexin Zhang, Junlin Li, Li Zhang, Jian Zhang, Shijie Zhao |
arXiv | Paper | Code |
| Residual Diffusion Bridge Model for Image Restoration Hebaixu Wang, Jing Zhang, Haoyang Chen, Haonan Guo, Di Wang, Jiayi Ma, Bo Du |
CVPR 2026 | Paper | Code |
| Hybrid Agents for Image Restoration Bingchen Li, Xin Li, Yiting Lu, Zhibo Chen |
CVPR 2026 | Paper | |
| UniLDiff: Unlocking the Power of Diffusion Priors for All-in-One Image Restoration Zihan Cheng, Liangtai Zhou, Dian Chen, Ni Tang, Xiaotong Luo, Yanyun Qu |
CVPR 2026 | Paper | |
| FAPE-IR: Frequency-Aware Planning and Execution Framework for All-in-One Image Restoration Jingren Liu, Shuning Xu, Qirui Yang, Yun Wang, Xiangyu Chen, Zhong Ji |
CVPR 2026 | Paper | Code |
| FoundIR-v2: Optimizing Pre-Training Data Mixtures for Image Restoration Foundation Model Xiang Chen, Jinshan Pan, Jiangxin Dong, Jian Yang, Jinhui Tang |
CVPR 2026 | Paper | Code |
| Degradation-Aware Adaptive Context Gating for Unified Image Restoration Lei He, Jielei Chu, Fengmao Lv, Weide Liu, Tianrui Li, Jun Cheng, Yuming Fang |
arXiv | Paper | Code |
| Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement Pu Li, Huafeng Li, Yafei Zhang, Yu Liu, Wen Wang |
CVPR 2026 | Paper | Code |
| LoViF 2026 Challenge on Real-World All-in-One Image Restoration: Methods and Results Xiang Chen, Hao Li, Jiangxin Dong, Jinshan Pan, et al. |
CVPR Workshops 2026 | Paper | |
| CDIR: LoRA-Inspired Attention for Efficient Composite Degradation Image Restoration Yuning Cui, Wenqi Ren, Boxin Shi, Jianhou Gan, Alois Knoll |
TIP 2026 | Paper | |
| A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting Yongchuan Cui, Peng Liu |
arXiv | Paper | Code |
| RIRF: Reasoning Image Restoration Framework Wending Yan, Rongkai Zhang, Kaihua Tang, Yu Cheng, Qiankun Liu |
arXiv | Paper | |
| WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression Weikai Qu, Sijun Liang, Cheng Pan, Zikuan Yang, Guanchi Zhou, Xianjun Fu, Bo Liu, Changmiao Wang, Ahmed Elazab |
TAI 2026 | Paper | Code |
| Task-Guided Prompting for Unified Remote Sensing Image Restoration Wenli Huang, Yang Wu, Xiaomeng Xin, Zhihong Liu, Jinjun Wang, Ye Deng |
TGRS 2026 | Paper | Code |
| Your Pre-trained Diffusion Model Secretly Knows Restoration Sudarshan Rajagopalan, Vishal M. Patel |
arXiv | Paper | Code |
| A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting Yongchuan Cui, Peng Liu |
arXiv | Paper | Code |
| TIR-Agent: Training an Explorative and Efficient Agent for Image Restoration Yisheng Zhang, Guoli Jia, Haote Hu, Shanxu Zhao, Kaikai Zhao, Long Sun, Xinwei Long, Kai Tian, Che Jiang, Zhaoxiang Liu, Kai Wang, Shiguo Lian, Kaiyan Zhang, Bowen Zhou |
arXiv | Paper | |
| Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration I-Hsiang Chen, Isma Hadji, Enrique Sanchez, Adrian Bulat, Sy-Yen Kuo, Radu Timofte, Georgios Tzimiropoulos, Brais Martinez |
CVPR 2026 | Paper | Code |
| Focal Modulation for Image Restoration Yuning Cui, Wenqi Ren, Alois Knoll |
IJCV 2026 | Paper | Code |
| StarIR: Convolutional Image Restoration With Spatial-Frequency Fusion Yuning Cui, Syed Waqas Zamir, Ming-Hsuan Yang, Alois Knoll, Fahad Shahbaz Khan, Salman Khan |
TPAMI 2026 | Paper | Code |
| Visual-in-Visual: A Unified and Efficient Baseline for Image Restoration Yuning Cui, Wenqi Ren, Boxin Shi, Alois Knoll |
TPAMI 2026 | Paper | |
| M2IR: Proactive All-in-One Image Restoration via Mamba-style Modulation and Mixture-of-Experts Shiwei Wang, Yongzhen Wang, Bingwen Hu, Liyan Zhang, Xiao-Ping Zhang, Mingqiang Wei |
arXiv | Paper | Code |
| UnSCAR: Universal, Scalable, Controllable, and Adaptable Image Restoration Debabrata Mandal, Soumitri Chattopadhyay, Yujie Wang, Marc Niethammer, Praneeth Chakravarthula |
arXiv | Paper | |
| SLER-IR: Spherical Layer-wise Expert Routing for All-in-One Image Restoration Peng Shurui, Xin Lin, Shi Luo, Jincen Ou, Dizhe Zhang, Lu Qi, Truong Nguyen, Chao Ren |
arXiv | Paper | |
| All-in-One Image Restoration via Causal-Deconfounding Wavelet-Disentangled Prompt Network Bingnan Wang, Bin Qin, Jiangmeng Li, Fanjiang Xu, Fuchun Sun, Hui Xiong |
TIP 2026 | Paper | |
| Learning Domain-Aware Task Prompt Representations for Multi-Domain All-in-One Image Restoration Guanglu Dong, Chunlei Li, Chao Ren, Jingliang Hu, Yilei Shi, Xiao Xiang Zhu, Lichao Mou |
ICLR 2026 | Paper | Code |
| MiM-DiT: MoE in MoE with Diffusion Transformers for All-in-One Image Restoration Lingshun Kong, Jiawei Zhang, Zhengpeng Duan, Xiaohe Wu, Yueqi Yang, Xiaotao Wang, Dongqing Zou, Lei Lei, Jinshan Pan |
arXiv | Paper | Code |
| Learning Continuous Wasserstein Barycenter Space for Generalized All-in-One Image Restoration Xiaole Tang, Xiaoyi He, Jiayi Xu, Xiang Gu, Jian Sun |
TPAMI 2026 | Paper | Code |
| PromptHSI: Universal Hyperspectral Image Restoration Framework for Composite Degradation Chia-Ming Lee, Ching-Heng Cheng, Yu-Fan Lin, Yi-Ching Cheng, Wo-Ting Liao, Chih-Chung Hsu, Fu-En Yang, Yu-Chiang Frank Wang |
TGRS 2026 | Paper | Code |
| HSI-VAR: Rethinking Hyperspectral Restoration through Spatial-Spectral Visual Autoregression Xiangming Wang, Benteng Sun, Yungeng Liu, Haijin Zeng, Yongyong Chen, Jingyong Su, Jie Liu |
arXiv | Paper | Code |
| Bridging Degradation Discrimination and Generation for Universal Image Restoration JiaKui Hu, Zhengjian Yao, Lujia Jin, Yanye Lu |
ICLR 2026 | Paper | Code |
| Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation Ping Chen, Zicheng Huang, Xiangming Wang, Yungeng Liu, Bingyu Liang, Haijin Zeng, Yongyong Chen |
arXiv | Paper | Code |
| DELNet: Continuous All-in-One Weather Removal via Dynamic Expert Library Shihong Liu, Kun Zuo, Hanguang Xiao |
ICASSP 2026 | Paper | |
| Unifying Heterogeneous Degradations: Uncertainty-Aware Diffusion Bridge Model for All-in-One Image Restoration Luwei Tu, Jiawei Wu, Xing Luo, Zhi Jin |
arXiv | Paper | |
| TPGDiff: Hierarchical Triple-Prior Guided Diffusion for Image Restoration Yanjie Tu, Qingsen Yan, Axi Niu, Jiacong Tang |
arXiv | Paper | Code |
| From Physical Degradation Models to Task-Aware All-in-One Image Restoration Hu Gao, Xiaoning Lei, Xichen Xu, Xingjian Wang, Lizhuang Ma |
arXiv | Paper | |
| Edit2Restore: Few-Shot Image Restoration via Parameter-Efficient Adaptation of Pre-trained Editing Models M. Akın Yılmaz, Ahmet Bilican, Burak Can Biner, A. Murat Tekalp |
arXiv | Paper | Code |
| UDPNet: Unleashing Depth-based Priors for Robust Image Dehazing Zengyuan Zuo, Junjun Jiang, Gang Wu, Xianming Liu |
arXiv | Paper | Code |
| ClearAIR: A Human-Visual-Perception-Inspired All-in-One Image Restoration Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang |
AAAI 2026 | Paper | Code |
| Gradient as Conditions: Rethinking HOG for All-in-one Image Restoration Jiawei Wu, Zhifei Yang, Zhe Wang, Zhi Jin |
AAAI 2026 | Paper | Code |
| EndoIR: Degradation-Agnostic All-in-One Endoscopic Image Restoration via Noise-Aware Routing Diffusion Tong Chen, Xinyu Ma, Long Bai, Wenyang Wang, Yue Sun, Luping Zhou |
AAAI 2026 | Paper | Code |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| Enhancing Image Restoration Transformer via Adaptive Translation Equivariance JiaKui Hu, Zhengjian Yao, Lujia Jin, Hangzhou He, Yanye Lu |
ICCV 2025 | Paper | |
| Degradation-Aware Prompted Transformer for Unified Medical Image Restoration Jinbao Wei, Gang Yang, Zhijie Wang, Shimin Tao, Aiping Liu, Xun Chen |
TIP 2025 | Paper | Code |
| SimpleCall: A Lightweight Image Restoration Agent in Label-Free Environments with MLLM Perceptual Feedback Jianglin Lu, Yuanwei Wu, Ziyi Zhao, Hongcheng Wang, Felix Jimenez, Abrar Majeedi, Yun Fu |
arXiv | Paper | |
| Degradation-Aware Metric Prompting for Hyperspectral Image Restoration Binfeng Wang, Di Wang, Haonan Guo, Ying Fu, Jing Zhang |
arXiv | Paper | Code |
| TAT: Task-Adaptive Transformer for All-in-One Medical Image Restoration Zhiwen Yang, Jiaju Zhang, Yang Yi, Jian Liang, Bingzheng Wei, Yan Xu |
MICCAI 2025 | Paper | Code |
| DSwinIR: Rethinking Window-Based Attention for Image Restoration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie |
TPAMI 2025 | Paper | Code |
| M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration |
TIP 2025 | Paper | |
| ClusIR: Towards Cluster-Guided All-in-One Image Restoration Shengkai Hu, Jiaqi Ma, Jun Wan, Wenwen Min, Yongcheng Jing, Lefei Zhang, Dacheng Tao |
arXiv | Paper | |
| Unleashing Degradation-Carrying Features in Symmetric U-Net: Simpler and Stronger Baselines for All-in-One Image Restoration Wenlong Jiao, Heyang Lee, Ping Wang, Pengfei Zhu, Qinghua Hu, Dongwei Ren |
arXiv | Paper | Code |
| EvoIR: Towards All-in-One Image Restoration via Evolutionary Frequency Modulation Jiaqi Ma, Shengkai Hu, Jun Wan, Jiaxing Huang, Lefei Zhang, Salman Khan |
arXiv | Paper | |
| Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie |
TPAMI 2025 | Paper | Code |
| Learning to Restore Multi-Degraded Images via Ingredient Decoupling and Task-Aware Path Adaptation Hu Gao, Xiaoning Lei, Ying Zhang, Xichen Xu, Guannan Jiang, Lizhuang Ma |
arXiv | Paper | |
| Physically Interpretable Multi-Degradation Image Restoration via Deep Unfolding and Explainable Convolution Hu Gao, Xiaoning Lei, Xichen Xu, Depeng Dang, Lizhuang Ma |
arXiv | Paper | |
| MOERL: When Mixture-of-Experts Meet Reinforcement Learning for Adverse Weather Image Restoration Tao Wang, Peiwen Xia, Bo Li, Peng-Tao Jiang, Zhe Kong, Kaihao Zhang, Tong Lu, Wenhan Luo |
ICCV 2025 | Paper | |
| Bio-Inspired Image Restoration Yuning Cui, Wenqi Ren, Alois Knoll |
NeurIPS 2025 | Paper | Code |
| A Minimalistic Unified Framework for Incremental Learning across Image Restoration Tasks Xiaoxuan Gong, Jie Ma |
NeurIPS 2025 | Paper | |
| Residual Diffusion Bridge Model for Image Restoration Hebaixu Wang, Jing Zhang, Haoyang Chen, Haonan Guo, Di Wang, Jiayi Ma, Bo Du |
arXiv | Paper | Code |
| UniUIR: Considering Underwater Image Restoration as An All-in-One Learner Xu Zhang, Huan Zhang, Guoli Wang, Qian Zhang, Lefei Zhang, Bo Du |
TIP 2025 | Paper | Code |
| Frequency-Prompted Image Restoration to Enhance Perception in Intelligent Transportation Systems Yuning Cui, Mingyu Liu, Xiongfei Su, Alois Knoll |
TITS 2025 | Paper | |
| Pruning Overparameterized Multi-Task Networks for Degraded Web Image Restoration Thomas Katraouras, Dimitrios Rafailidis |
WI-IAT 2025 | Paper | Code |
| Latent Harmony: Synergistic Unified UHD Image Restoration via Latent Space Regularization and Controllable Refinement Yidi Liu, Xueyang Fu, Jie Huang, Jie Xiao, Dong Li, Wenlong Zhang, Lei Bai, Zheng-Jun Zha |
NeurIPS 2025 | Paper | Code |
| UHD-Processor: Unified UHD Image Restoration with Progressive Frequency Learning and Degradation-aware Prompts Yidi Liu, Xueyang Fu, Jie Huang, Jie Xiao, Dong Li, Wenlong Zhang, Lei Bai, Zheng-Jun Zha |
CVPR 2025 | Paper | Code |
| Universal Image Restoration Pre-training via Masked Degradation Classification JiaKui Hu, Zhengjian Yao, Lujia Jin, Yinghao Chen, Yanye Lu |
arXiv | Paper | Code |
| Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation Vijay M. Galshetwar, Praful Hambarde, Prashant W. Patil, Akshay Dudhane, Sachin Chaudhary, Santosh Kumar Vipparathi, Subrahmanyam Murala |
arXiv | Paper | Code |
| PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration Zhe Dong, Yuzhe Sun, Haochen Jiang, Tianzhu Liu, Yanfeng Gu |
arXiv | Paper | Code |
| Orochi: Versatile Biomedical Image Processor Gaole Dai, Chenghao Zhou, Yu Zhou, Rongyu Zhang, Yuan Zhang, Chengkai Hou, Tiejun Huang, Jianxu Chen, Shanghang Zhang |
NeurIPS 2025 | Paper | |
| Degradation-Aware All-in-One Image Restoration via Latent Prior Encoding S M A Sharif, Abdur Rehman, Fayaz Ali Dharejo, Radu Timofte, Rizwan Ali Naqvi |
arXiv | Paper | Code |
| JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration Yunlong Lin, Zixu Lin, Haoyu Chen, Panwang Pan, Chenxin Li, Sixiang Chen, Kairun Wen, Yeying Jin, Wenbo Li, Xinghao Ding |
CVPR 2025 | Paper | Code |
| RAM++: Robust Representation Learning via Adaptive Mask for All-in-One Image Restoration Zilong Zhang, Chujie Qin, Chunle Guo, Yong Zhang, Chao Xue, Ming-Ming Cheng, Chongyi Li |
arXiv | Paper | Code |
| WeatherBench: A Real-World Benchmark Dataset for All-in-One Adverse Weather Image Restoration Qiyuan Guan, Qianfeng Yang, Xiang Chen, Tianyu Song, Guiyue Jin, Jiyu Jin |
ACMMM 2025 | Paper | Code |
| Modumer: Modulating Transformer for Image Restoration Yuning Cui, Mingyu Liu, Wenqi Ren, Alois Knoll |
TNNLS 2025 | Paper | |
| TAP: Parameter-efficient Task-Aware Prompting for Adverse Weather Removal Hanting Wang, Shengpeng Ji, Shulei Wang, Hai Huang, Xiao Jin, Qifei Zhang, Tao Jin |
arXiv | Paper | |
| Diffusion Once and Done: Degradation‑Aware LoRA for Efficient All‑in‑One Image Restoration Ni Tang, Xiaotong Luo, Zihan Cheng, Liangtai Zhou, Dongxiao Zhang, Yanyun Qu |
arXiv | Paper | |
| Exploiting Diffusion Prior for Task-driven Image Restoration Jaeha Kim, Junghun Oh, Kyoung Mu Lee |
ICCV 2025 | Paper | Code |
| Robust Adverse Weather Removal via Spectral-based Spatial Grouping Yuhwan Jeong, Yunseo Yang, Youngjo Yoon, Kuk-Jin Yoon |
ICCV 2025 | Paper | Code |
| All-in-One Medical Image Restoration with Latent Diffusion-Enhanced Vector-Quantized Codebook Prior Haowei Chen, Zhiwen Yang, Haotian Hou, Hui Zhang, Bingzheng Wei, Gang Zhou, Yan Xu |
MICCAI 2025 | Paper | |
| Exploring Scalable Unified Modeling for General Low-Level Vision Xiangyu Chen, Kaiwen Zhu, Yuandong Pu, Shuo Cao, Xiaohui Li, Wenlong Zhang, Yihao Liu, Yu Qiao, Jiantao Zhou, Chao Dong |
arXiv | Paper | |
| Exploring the Potential of Pooling Techniques for Universal Image Restoration Yuning Cui, Wenqi Ren, Alois Knoll |
TIP 2025 | Paper | Code |
| 4KAgent: Agentic Any Image to 4K Super-Resolution Yushen Zuo, Qi Zheng, Mingyang Wu, Xinrui Jiang, Renjie Li, Jian Wang, Yide Zhang, Gengchen Mai, Lihong V. Wang, James Zou, Xiaoyu Wang, Ming-Hsuan Yang, Zhengzhong Tu |
NeurIPS 2025 | Paper | Code |
| Elucidating and Endowing the Diffusion Training Paradigm for General Image Restoration Xin Lu, Xueyang Fu, Jie Xiao, Zihao Fan, Yurui Zhu, Zheng-Jun Zha |
arXiv | Paper | Code |
| LD-RPS: Zero-Shot Unified Image Restoration via Latent Diffusion Recurrent Posterior Sampling Huaqiu Li, Yong Wang, Tongwen Huang, Hailang Huang, Haoqian Wang, Xiangxiang Chu |
arXiv | Paper | Code |
| FoundIR: Unleashing Million-scale Training Data to Advance Foundation Models for Image Restoration Hao Li, Xiang Chen, Jiangxin Dong, Jinhui Tang, Jinshan Pan |
ICCV 2025 | Paper | Code |
| UniRestore: Unified Perceptual and Task-Oriented Image Restoration Model Using Diffusion Prior I-Hsiang Chen, Wei-Ting Chen, Yu-Wei Liu, Yuan-Chun Chiang, Sy-Yen Kuo, Ming-Hsuan Yang |
CVPR 2025 | Paper | Code |
| Visual-Instructed Degradation Diffusion for All-in-One Image Restoration Haina Qin, Wenyang Luo, Zewen Chen, Yufan Liu, Bing Li, Weiming Hu, libin wang, DanDan Zheng, Yuming Li |
CVPR 2025 | Paper | Code |
| UniRes: Universal Image Restoration for Complex Degradations |
arXiv | Paper | |
| Boosting All-in-One Image Restoration via Self-Improved Privilege Learning |
arXiv | Paper | Code |
| Navigating Image Restoration with VAR's Distribution Alignment Prior Siyang Wang, Feng Zhao |
arXiv | Paper | Code |
| From Controlled Scenarios to Real-World: Cross-Domain Degradation Pattern Matching for All-in-One Image Restoration Junyu Fan, Chuanlin Liao, Yi Lin |
arXiv | Paper | |
| Manifold-aware Representation Learning for Degradation-agnostic Image Restoration Bin Ren, Yawei Li, Xu Zheng, Yuqian Fu, Danda Pani Paudel, Ming-Hsuan Yang, Luc Van Gool, Nicu Sebe |
arXiv | Paper | Code |
| Learning Dynamic Prompts for All-in-One Image Restoration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie |
TIP 2025 | Paper | Code |
| MODEM: A Morton-Order Degradation Estimation Mechanism for Adverse Weather Image Recovery Hainuo Wang, Qiming Hu, Xiaojie Guo |
arXiv | Paper | Code |
| Clear Nights Ahead: Towards Multi-Weather Nighttime Image Restoration Yuetong Liu, Yunqiu Xu, Yang Wei, Xiuli Bi, Bin Xiao |
arXiv | Paper | Code |
| RestoreVAR: Visual Autoregressive Generation for All-in-One Image Restoration Sudarshan Rajagopalan, Kartik Narayan, Vishal M. Patel |
arXiv | Paper | Code |
| All-in-One Weather-Degraded Image Restoration Via Adaptive Degradation-Aware Self-Prompting Model Yuanbo Wen, Tao Gao, Ziqi Li, Jing Zhang, Kaihao Zhang, Ting Chen |
TMM 2025 | Paper | |
| AWRaCLe: All-Weather Image Restoration using Visual In-Context Learning Sudarshan Rajagopalan, Vishal M. Patel |
AAAI 2025 | Paper | Code |
| CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture Vladimir Frants, Sos Agaian, Karen Panetta, Peter Huang |
arXiv | Paper | |
| DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model Zhanwen Liu, Sai Zhou, Yuchao Dai, Yang Wang, Yisheng An, Xiangmo Zhao |
arXiv | Paper | |
| Any Image Restoration via Efficient Spatial-Frequency Degradation Adaptation Bin Ren, Eduard Zamfir, Zongwei Wu, Yawei Li, Yidi Li, Danda Pani Paudel, Radu Timofte, Ming-Hsuan Yang, Luc Van Gool, Nicu Sebe |
arXiv | Paper | Code |
| Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu, Liqiang Nie |
arXiv | Paper | Code |
| Beyond Degradation Conditions: All-in-One Image Restoration via HOG Transformers Jiawei Wu, Zhifei Yang, Zhe Wang, Zhi Jin |
arXiv | Paper | Code |
| Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision Yuandong Pu, Le Zhuo, Kaiwen Zhu, Liangbin Xie, Wenlong Zhang, Xiangyu Chen, Peng Gao, Yu Qiao, Chao Dong, Yihao Liu |
arXiv | Paper | Code |
| Multi-axis Prompt and Multi-dimension Fusion Network for All-in-one Weather-degraded Image Restoration Yuanbo Wen, Tao Gao, Jing Zhang, Ziqi Li, Ting Chen |
AAAI 2025 | Paper | Code |
| VL-UR: Vision-Language-guided Universal Restoration of Images Degraded by Adverse Weather Conditions Ziyan Liu, Yuxu Lu, Huashan Yu, Dong Yang |
arXiv | Paper | |
| Q-Agent: Quality-Driven Chain-of-Thought Image Restoration Agent through Robust Multimodal Large Language Model Yingjie Zhou, Jiezhang Cao, Zicheng Zhang, Farong Wen, Yanwei Jiang, Jun Jia, Xiaohong Liu, Xiongkuo Min, Guangtao Zhai |
arXiv | Paper | |
| DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration Jiamei Xiong, Xuefeng Yan, Yongzhen Wang, Wei Zhao, Xiao-Ping Zhang, Mingqiang Wei |
arXiv | Paper | |
| Content-Aware Transformer for All-in-one Image Restoration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu |
arXiv | Paper | Code |
| Cat-AIR: Content and Task-Aware All-in-One Image Restoration Jiachen Jiang, Tianyu Ding, Ke Zhang, Jinxin Zhou, Tianyi Chen, Ilya Zharkov, Zhihui Zhu, Luming Liang |
arXiv | Paper | Code |
| UniCoRN: Latent Diffusion-based Unified Controllable Image Restoration Network across Multiple Degradations Debabrata Mandal, Soumitri Chattopadhyay, Guansen Tong, Praneeth Chakravarthula |
arXiv | Paper | Code |
| Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal Rongxin Liao, Feng Li, Yanyan Wei, Zenglin Shi, Le Zhang, Huihui Bai, Meng Wang |
arXiv | Paper | Code |
| TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models Wanglong Lu, Lingming Su, Jingjing Zheng, Vinícius Veloso de Melo, Farzaneh Shoeleh, John Hawkin, Terrence Tricco, Hanli Zhao, Xianta Jiang |
arXiv | Paper | |
| Vision-Language Gradient Descent-driven All-in-One Deep Unfolding Networks Haijin Zeng, Xiangming Wang, Yongyong Chen, Jingyong Su, Jie Liu |
CVPR2025 | Paper | Code |
| Degradation-Aware Feature Perturbation for All-in-One Image Restoration Xiangpeng Tian, Xiangyu Liao, Xiao Liu, Meng Li, Chao Ren |
CVPR2025 | Paper | Code |
| Multi-Agent Image Restoration Xu Jiang, Gehui Li, Bin Chen, Jian Zhang |
arXiv | Paper | |
| MP-HSIR: A Multi-Prompt Framework for Universal Hyperspectral Image Restoration Zhehui Wu, Yong Chen, Naoto Yokoya, Wei He |
arXiv | Paper | Code |
| Dynamic Degradation Decomposition Network for All-in-One Image Restoration Huiqiang Wang, Mingchen Song, Guoqiang Zhong |
arXiv | Paper | |
| USRNet: Unified Scene Recovery Network for Enhancing Traffic Imaging under Multiple Adverse Weather Conditions Yuxu Lu, Ai Chen, Dong Yang, Ryan Wen Liu |
arXiv | Paper | Code |
| Up-Restorer: When Unrolling Meets Prompts for Unified Image Restoration Minghao Liu, Wenhan Yang, Jinyi Luo, Jiaying Liu |
AAAI2025 | Paper | |
| Debiased All-in-one Image Restoration with Task Uncertainty Regularization Gang Wu, Junjun Jiang, Yijun Wang, Kui Jiang, Xianming Liu |
AAAI2025 | Paper | Code |
| All-in-One Image Compression and Restoration Huimin Zeng, Jiacheng Li, Ziqiang Zheng, Zhiwei Xiong |
WACV2025 | Paper | Code |
| RamIR: Reasoning and action prompting with Mamba for all-in-one image restoration Aiqiang Tang, Yan Wu, Yuwei Zhang |
Applied Intelligence | Paper | |
| Universal Image Restoration Pre-training via Degradation Classification JiaKui Hu, Lujia Jin, Zhengjian Yao, Yanye Lu |
ICLR2025 | Paper | Code |
| Complexity Experts are Task-Discriminative Learners for Any Image Restoration Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yuedong Tan, Danda Pani Paudel, Yulun Zhang, Radu Timofte |
CVPR2025 | Paper | Code |
| Adaptive Blind All-in-One Image Restoration David Serrano-Lozano, Luis Herranz, Shaolin Su, Javier Vazquez-Corral |
arXiv | Paper | Code |
| GenDeg: Diffusion-Based Degradation Synthesis for Generalizable All-in-One Image Restoration Sudarshan Rajagopalan, Nithin Gopalakrishnan Nair, Jay N. Paranjape, Vishal M. Patel |
CVPR2025 | Paper | Code |
| OmniRestore: Robust Universal Image Restoration from Combined and Unspecified Degradations Anjusree Karnavar, Yang Li, Jiajun Liu, Jun Zhou, Junhu Wang |
ICME 2025 | Paper |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| Reference-Based Multi-Stage Progressive Restoration for Multi-Degraded Images Yi Zhang, Qixue Yang, Damon M. Chandler, Xuanqin Mou |
TIP 2024 | Paper | Code |
| IPT-ILR: Image Pyramid Transformer Coupled With Information Loss Regularization for All-in-One Image Restoration Sai Yang, Bin Hu, Fan Liu, Xiaoxin Wu, Weiping Ding, Jun Zhou |
TCSVT 2024 | Paper | |
| Omni-Kernel Modulation for Universal Image Restoration Yuning Cui, Wenqi Ren, Alois Knoll |
TCSVT 2024 | Paper | |
| UniRestorer: Universal Image Restoration via Adaptively Estimating Image Degradation at Proper Granularity Jingbo Lin, Zhilu Zhang, Wenbo Li, Renjing Pei, Hang Xu, Hongzhi Zhang, Wangmeng Zuo |
arXiv | Paper | Code |
| Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation Aiwen Jiang, Hourong Chen, Zhiwen Chen, Jihua Ye, Mingwen Wang |
arXiv | Paper | Code |
| MWFormer: Multi-Weather Image Restoration Using Degradation-Aware Transformers Ruoxi Zhu, Zhengzhong Tu, Jiaming Liu, Alan C. Bovik, Yibo Fan |
TIP2024 | Paper | Code |
| Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding Yubin Gu, Yuan Meng, Xiaoshuai Sun, Jiayi Ji, Weijian Ruan, Rongrong Ji |
arXiv | Paper | |
| AllRestorer: All-in-One Transformer for Image Restoration under Composite Degradations Jiawei Mao, Yu Yang, Xuesong Yin, Ling Shao, Hao Tang |
arXiv | Paper | |
| All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model Yuanbo Wen, Tao Gao, Ziqi Li, Jing Zhang, Kaihao Zhang, Ting Chen |
arXiv | Paper | |
| Degradation-Aware Residual-Conditioned Optimal Transport for Unified Image Restoration Xiaole Tang, Xiang Gu, Xiaoyi He, Xin Hu, Jian Sun |
TPAMI2025 | Paper | Code |
| Chain-of-Restoration: Multi-Task Image Restoration Models are Zero-Shot Step-by-Step Universal Image Restorers Jin Cao, Deyu Meng, Xiangyong Cao |
arXiv | Paper | Code |
| An Intelligent Agentic System for Complex Image Restoration Problems Kaiwen Zhu, Jinjin Gu, Zhiyuan You, Yu Qiao, Chao Dong |
ICLR2025 | Paper | Code |
| LoRA-IR: Taming Low-Rank Experts for Efficient All-in-One Image Restoration Yuang Ai, Huaibo Huang, Ran He |
arXiv | Paper | Code |
| Adaptive Fuzzy Degradation Perception Based on CLIP Prior for All-in-One Image Restoration M. Shao, Y. Liu, Y. Cheng, Y. Wan, C. Wang |
IEEE Trans. Fuzzy Syst 2024 | Paper | |
| TANet: Triplet Attention Network for All-In-One Adverse Weather Image Restoration Hsing-Hua Wang, Fu-Jen Tsai, Yen-Yu Lin, Chia-Wen Lin |
ACCV2024 | Paper | Code |
| Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration Chu-Jie Qin, Rui-Qi Wu, Zikun Liu, Xin Lin, Chun-Le Guo, Hyun Hee Park, Chongyi Li |
ECCV2024 | Paper | Code |
| Teaching Tailored to Talent: Adverse Weather Restoration via Prompt Pool and Depth-Anything Constraint Sixiang Chen, Tian Ye, Kai Zhang, Zhaohu Xing, Yunlong Lin, Lei Zhu |
ECCV2024 | Paper | Code |
| Accurate Forgetting for All-in-One Image Restoration Model Xin Su, Zhuoran Zheng |
arXiv | Paper | |
| Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration Xu Zhang, Jiaqi Ma, Guoli Wang, Qian Zhang, Huan Zhang, Lefei Zhang |
TIP2025 | Paper | Code |
| Learning A Low-Level Vision Generalist via Visual Task Prompt Xiangyu Chen, Yihao Liu, Yuandong Pu, Wenlong Zhang, Jiantao Zhou, Yu Qiao, Chao Dong |
ACMMM2024 | Paper | Code |
| HAIR: Hypernetworks-based All-in-One Image Restoration Jin Cao, Yi Cao, Li Pang, Deyu Meng, Xiangyong Cao |
arXiv | Paper | Code |
| Review Learning: Advancing All-in-One Ultra-High-Definition Image Restoration Training Method Xin Su, Zhuoran Zheng, Chen Wu |
arXiv | Paper | |
| UniProcessor: A Text-induced Unified Low-level Image Processor Huiyu Duan, Xiongkuo Min, Sijing Wu, Wei Shen, Guangtao Zhai |
ECCV2024 | Paper | Code |
| Multi-Expert Adaptive Selection: Task-Balancing for All-in-One Image Restoration Xiaoyan Yu, Shen Zhou, Huafeng Li, Liehuang Zhu |
TCSVT 2024 | Paper | Code |
| RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Sixiang Chen, Tian Ye, Renjing Pei, Kaiwen Zhou, Fenglong Song, Lei Zhu |
NIPS2024 | Paper | Code |
| Training-Free Large Model Priors for Multiple-in-One Image Restoration Xuanhua He, Lang Li, Yingying Wang, Hui Zheng, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou |
arXiv | Paper | |
| Diff-Restorer: Unleashing Visual Prompts for Diffusion-based Universal Image Restoration Yuhong Zhang, Hengsheng Zhang, Xinning Chai, Zhengxue Cheng, Rong Xie, Li Song, Wenjun Zhang |
arXiv | Paper | Code |
| Any Image Restoration with Efficient Automatic Degradation Adaptation Bin Ren, Eduard Zamfir, Yawei Li, Zongwei Wu, Danda Pani Paudel, Radu Timofte, Nicu Sebe, Luc Van Gool |
arXiv | Paper | Code |
| Restoring Images in Adverse Weather Conditions via Histogram Transformer Shangquan Sun, Wenqi Ren, Xinwei Gao, Rui Wang, Xiaochun Cao |
ECCV 2024 | Paper | Code |
| GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity Shuo Cao, Yihao Liu, Wenlong Zhang, Yu Qiao, Chao Dong |
ECCV2024 | Paper | |
| OneRestore: A Universal Restoration Framework for Composite Degradation Yu Guo, Yuan Gao, Yuxu Lu, Huilin Zhu, Ryan Wen Liu, Shengfeng He |
ECCV2024 | Paper | Code |
| Learning Frequency-Aware Dynamic Transformers for All-In-One Image Restoration Zenglin Shi, Tong Su, Pei Liu, Yunpeng Wu, Le Zhang, Meng Wang |
arXiv | Paper | |
| Instruct-IPT: All-in-One Image Processing Transformer via Weight Modulation Yuchuan Tian, Jianhong Han, Hanting Chen, Yuanyuan Xi, Guoyang Zhang, Jie Hu, Chao Xu, Yunhe Wang |
arXiv | Paper | Code |
| ConStyle v2: A Strong Prompter for All-in-One Image Restoration Dongqi Fan, Junhao Zhang, Liang Chang |
arXiv | Paper | Code |
| Restorer: Removing Multi-Degradation with All-Axis Attention and Prompt Guidance Jiawei Mao, Juncheng Wu, Yuyin Zhou, Xuesong Yin, Yuanqi Chang |
arXiv | Paper | Code |
| All-In-One Medical Image Restoration via Task-Adaptive Routing Zhiwen Yang, Haowei Chen, Ziniu Qian, Yang Yi, Hui Zhang, Dan Zhao, Bingzheng Wei, Yan Xu |
MICCAI2024 | Paper | Code |
| Efficient Degradation-aware Any Image Restoration Eduard Zamfir, Zongwei Wu, Nancy Mehta, Danda Pani Paudel, Yulun Zhang, Radu Timofte |
arXiv | Paper | Code |
| Harmony in Diversity: Improving All-in-One Image Restoration via Multi-Task Collaboration Gang Wu, Junjun Jiang, Kui Jiang, Xianming Liu |
ACMMM2024 | Paper | Code |
| Referring Flexible Image Restoration Runwei Guan, Rongsheng Hu, Zhuhao Zhou, Tianlang Xue, Ka Lok Man, Jeremy Smith, Eng Gee Lim, Weiping Ding, Yutao Yue |
arXiv | Paper | Code |
| Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models Ziwei Luo, Fredrik K. Gustafsson, Zheng Zhao, Jens Sjölund, Thomas B. Schön |
CVPRW2024 | Paper | Code |
| Joint Conditional Diffusion Model for Image Restoration with Mixed Degradations Yufeng Yue, Meng Yu, Luojie Yang, Yi Yang |
arXiv | Paper | |
| Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration Akshay Dudhane, Omkar Thawakar, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan, Ming-Hsuan Yang |
arXiv | Paper | Code |
| WM-MoE: Weather-aware Multi-scale Mixture-of-Experts for Blind Adverse Weather Removal Yulin Luo, Rui Zhao, Xiaobao Wei, Jinwei Chen, Yijie Lu, Shenghao Xie, Tianyu Wang, Ruiqin Xiong, Ming Lu, Shanghang Zhang |
arXiv | Paper | |
| AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation Yuning Cui, Syed Waqas Zamir, Salman Khan, Alois Knoll, Mubarak Shah, Fahad Shahbaz Khan |
ICLR2025 | Paper | Code |
| DocRes: A Generalist Model Toward Unifying Document Image Restoration Tasks Jiaxin Zhang, Dezhi Peng, Chongyu Liu, Peirong Zhang, Lianwen Jin |
CVPR 2024 | Paper | Code |
| Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model Dian Zheng, Xiao-Ming Wu, Shuzhou Yang, Jian Zhang, Jian-Fang Hu, Wei-Shi Zheng |
CVPR2024 | Paper | Code |
| Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure De Cheng, Yanling Ji, Dong Gong, Yan Li, Nannan Wang, Junwei Han, Dingwen Zhang |
TMM 2024 | Paper | Code |
| AoSRNet: All-in-One Scene Recovery Networks via Multi-knowledge Integration Yuxu Lu, Dong Yang, Yuan Gao, Ryan Wen Liu, Jun Liu, Yu Guo |
arXiv | Paper | Code |
| InstructIR: High-Quality Image Restoration Following Human Instructions Marcos V. Conde, Gregor Geigle, Radu Timofte |
ECCV2024 | Paper | Code |
| Unified-Width Adaptive Dynamic Network for All-In-One Image Restoration Yimin Xu, Nanxi Gao, Zhongyun Shan, Fei Chao, Rongrong Ji |
arXiv | Paper | Code |
| Towards Effective Multiple-in-One Image Restoration: A Sequential and Prompt Learning Strategy Xiangtao Kong, Chao Dong, Lei Zhang |
arXiv | Paper | Code |
| Improving Image Restoration through Removing Degradations in Textual Representations Jingbo Lin, Zhilu Zhang, Yuxiang Wei, Dongwei Ren, Dongsheng Jiang, Wangmeng Zuo |
CVPR2024 | Paper | Code |
| Efficient Deweather Mixture-of-Experts with Uncertainty-aware Feature-wise Linear Modulation Rongyu Zhang, Yulin Luo, Jiaming Liu, Huanrui Yang, Zhen Dong, Denis Gudovskiy, Tomoyuki Okuno, Yohei Nakata, Kurt Keutzer, Yuan Du, Shanghang Zhang |
AAAI2024 | Paper | Code |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| Textual Prompt Guided Image Restoration Qiuhai Yan, Aiwen Jiang, Kang Chen, Long Peng, Qiaosi Yi, Chunjie Zhang |
arXiv | Paper | Code |
| Decoupling Degradation and Content Processing for Adverse Weather Image Restoration Xi Wang, Xueyang Fu, Peng-Tao Jiang, Jie Huang, Mi Zhou, Bo Li, Zheng-Jun Zha |
arXiv | Paper | |
| Multimodal Prompt Perceiver: Empower Adaptiveness, Generalizability and Fidelity for All-in-One Image Restoration Yuang Ai, Huaibo Huang, Xiaoqiang Zhou, Jiexiang Wang, Ran He |
CVPR2024 | Paper | |
| Language-driven All-in-one Adverse Weather Removal Hao Yang, Liyuan Pan, Yan Yang, Wei Liang |
CVPR2024 | Paper | |
| Test-Time Degradation Adaptation for Open-Set Image Restoration Yuanbiao Gou, Haiyu Zhao, Boyun Li, Xinyan Xiao, Xi Peng |
ICML2024 | Paper | Code |
| Clarity ChatGPT: An Interactive and Adaptive Processing System for Image Restoration and Enhancement Yanyan Wei, Zhao Zhang, Jiahuan Ren, Xiaogang Xu, Richang Hong, Yi Yang, Shuicheng Yan, Meng Wang |
arXiv | Paper | |
| Always Clear Days: Degradation Type and Severity Aware All-In-One Adverse Weather Removal Yu-Wei Chen, Soo-Chang Pei |
IEEE Access2025 | Paper | Code |
| Neural Degradation Representation Learning for All-In-One Image Restoration Mingde Yao, Ruikang Xu, Yuanshen Guan, Jie Huang, Zhiwei Xiong |
TIP2024 | Paper | |
| AutoDIR: Automatic All-in-One Image Restoration with Latent Diffusion Yitong Jiang, Zhaoyang Zhang, Tianfan Xue, Jinwei Gu |
ECCV2024 | Paper | Code |
| Unifying Image Processing as Visual Prompting Question Answering Yihao Liu, Xiangyu Chen, Xianzheng Ma, Xintao Wang, Jiantao Zhou, Yu Qiao, Chao Dong |
ICML2024 | Paper | |
| Controlling Vision-Language Models for Universal Image Restoration Ziwei Luo, Zheng Zhao, Thomas B. Schön |
ICLR2024 | Paper | Code |
| Fill the K-Space and Refine the Image: Prompting for Dynamic and Multi-Contrast MRI Reconstruction Bingyu Xin, Meng Ye, Leon Axel, Dimitris N. Metaxas |
STACOM2023 | Paper | Code |
| Prompt-based Ingredient-Oriented All-in-One Image Restoration Hu Gao, Depeng Dang |
TCSVT2024 | Paper | |
| All-in-one Multi-degradation Image Restoration Network via Hierarchical Degradation Representation Cheng Zhang, Yu Zhu, Qingsen Yan, Jinqiu Sun, Yanning Zhang |
ACMMM2023 | Paper | |
| Decomposition Ascribed Synergistic Learning for Unified Image Restoration Jinghao Zhang, Feng Zhao |
arXiv | Paper | |
| RDM-IR: Task-Adaptive Deep Unfolding Network for All-In-One Image Restoration Yuanshuo Cheng, Mingwen Shao, Yecong Wan, Chao Wang |
Knowledge-Based Systems2024 | Paper | Code |
| ProRes: Exploring Degradation-aware Visual Prompt for Universal Image Restoration Jiaqi Ma, Tianheng Cheng, Guoli Wang, Qian Zhang, Xinggang Wang, Lefei Zhang |
arXiv | Paper | |
| PromptIR: Prompting for All-in-One Blind Image Restoration Vaishnav Potlapalli, Syed Waqas Zamir, Salman Khan, Fahad Shahbaz Khan |
NeurIPS2023 | Paper | Code |
| GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions Tao Wang, Kaihao Zhang, Ziqian Shao, Wenhan Luo, Bjorn Stenger, Tong Lu, Tae-Kyun Kim, Wei Liu, Hongdong Li |
IJCV2024 | Paper | Code |
| Multi-task Image Restoration Guided By Robust DINO Features Xin Lin, Jingtong Yue, Kelvin C.K. Chan, Lu Qi, Chao Ren, Jinshan Pan, Ming-Hsuan Yang |
arXiv | Paper | |
| Generative Diffusion Prior for Unified Image Restoration and Enhancement Ben Fei, Zhaoyang Lyu, Liang Pan, Junzhe Zhang, Weidong Yang, Tianyue Luo, Bo Zhang, Bo Dai |
CVPR2023 | Paper | Code |
| Context-aware Pretraining for Efficient Blind Image Decomposition Chao Wang, Zhedong Zheng, Ruijie Quan, Yifan Sun, Yi Yang |
CVPR2023 | Paper | |
| Ingredient-oriented Multi-Degradation Learning for Image Restoration Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao |
CVPR2023 | Paper | |
| Learning Weather-General and Weather-Specific Features for Image Restoration Under Multiple Adverse Weather Conditions Yurui Zhu, Tianyu Wang, Xueyang Fu, Xuanyu Yang, Xin Guo, Jifeng Dai, Yu Qiao, Xiaowei Hu |
CVPR2023 | Paper | Code |
| Adverse Weather Removal with Codebook Priors Tian Ye, Sixiang Chen, Jinbin Bai, Jun Shi, Chenghao Xue, Jingxia Jiang, Junjie Yin, Erkang Chen, Yun Liu |
ICCV2023 | Paper | |
| Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models Ozan Özdenizci, Robert Legenstein |
TPAMI2023 | Paper | |
| Images Speak in Images: A Generalist Painter for In-Context Visual Learning Xinlong Wang, Wen Wang, Yue Cao, Chunhua Shen, Tiejun Huang |
CVPR2023 | Paper | Code |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| All-In-One Image Restoration for Unknown Corruption Boyun Li, Xiao Liu, Peng Hu, Zhongqin Wu, Jiancheng Lv, Xi Peng |
CVPR2022 | Paper | Code |
| TAPE: Task-Agnostic Prior Embedding for Image Restoration Lin Liu, Lingxi Xie, Xiaopeng Zhang, Shanxin Yuan, Xiangyu Chen, Wengang Zhou, Houqiang Li, Qi Tian |
CVPR2022 | Paper | |
| Learning Multiple Adverse Weather Removal via Two-stage Knowledge Learning and Multi-contrastive Regularization: Toward a Unified Model Wei-Ting Chen, Zhi-Kai Huang, Cheng-Che Tsai, Hao-Hsiang Yang, Jian-Jiun Ding, Sy-Yen Kuo |
CVPR2022 | Paper | Code |
| TransWeather: Transformer-based Restoration of Images Degraded by Adverse Weather Conditions Jeya Maria Jose Valanarasu, Rajeev Yasarla, Vishal M. Patel |
CVPR2022 | Paper | Code |
| Blind Image Decomposition Junlin Han, Weihao Li, Pengfei Fang, Chunyi Sun, Jie Hong, Mohammad Ali Armin, Lars Petersson, Hongdong Li |
ECCV2022 | Paper | Code |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| On Efficient Transformer-Based Image Pre-training for Low-Level Vision Wenbo Li, Xin Lu, Shengju Qian, Jiangbo Lu, Xiangyu Zhang, Jiaya Jia |
arXiv | Paper | Code |
| Pre-Trained Image Processing Transformer Hanting Chen, Yunhe Wang, Tianyu Guo, Chang Xu, Yiping Deng, Zhenhua Liu, Siwei Ma, Chunjing Xu, Chao Xu, Wen Gao |
CVPR2021 | Paper | Code |
| Paper | Avenue | Link | Code |
|---|---|---|---|
| All in One Bad Weather Removal Using Architectural Search Ruoteng Li, Robby T. Tan, Loong-Fah Cheong |
CVPR2020 | Paper | |
| A General Decoupled Learning Framework for Parameterized Image Operators Qingnan Fan, Dongdong Chen, Lu Yuan, Gang Hua, Nenghai Yu, Baoquan Chen |
TPAMI2019 | Paper |
| Category | Method | Venue & Year | Params | Dehazing SOTS | Deraining Rain100L | Denoising σ=15 | Denoising σ=25 | Denoising σ=50 | Average | Approach |
|---|---|---|---|---|---|---|---|---|---|---|
| Single | LPN | CVPR'19 | 3M | 20.84/0.828 | 24.88/0.784 | 26.47/0.778 | 24.77/0.748 | 21.26/0.552 | 23.64/0.738 | Specific |
| ADFNet | AAAI'23 | 8M | 28.13/0.961 | 34.24/0.965 | 33.76/0.929 | 30.83/0.871 | 27.75/0.793 | 30.94/0.904 | Specific | |
| DehazeFormer | TIP'23 | 25M | 29.58/0.970 | 35.37/0.969 | 33.01/0.914 | 30.14/0.858 | 27.37/0.779 | 31.09/0.898 | Specific | |
| DRSformer | CVPR'23 | 34M | 29.02/0.968 | 35.89/0.970 | 33.28/0.921 | 30.55/0.862 | 27.58/0.786 | 31.26/0.902 | Specific | |
| Multiple | MPRNet | CVPR'21 | 16M | 28.00/0.958 | 33.86/0.958 | 33.27/0.920 | 30.76/0.871 | 27.29/0.761 | 30.63/0.894 | General |
| Restormer | CVPR'22 | 26M | 27.78/0.958 | 33.78/0.958 | 33.72/0.865 | 30.67/0.865 | 27.63/0.792 | 30.75/0.901 | General | |
| NAFNet | ECCV'22 | 17M | 24.11/0.960 | 33.64/0.956 | 33.18/0.918 | 30.47/0.865 | 27.12/0.754 | 29.67/0.844 | General | |
| FSNet | TPAMI'23 | 13M | 29.14/0.969 | 35.61/0.969 | 33.81/0.874 | 30.84/0.872 | 27.69/0.762 | 31.42/0.906 | General | |
| MambaIR | ECCV'24 | 27M | 29.57/0.970 | 35.42/0.969 | 33.88/0.931 | 30.95/0.874 | 27.74/0.793 | 31.51/0.907 | General | |
| All-in-One | DL | TPAMI'19 | 2M | 26.92/0.931 | 32.62/0.931 | 33.05/0.914 | 30.41/0.861 | 26.90/0.740 | 29.98/0.875 | parameterized image operator |
| TKMANet | CVPR'22 | 29M | 30.41/0.973 | 34.94/0.972 | 33.02/0.924 | 30.31/0.820 | 23.80/0.556 | 30.50/0.849 | two-stage knowledge learning | |
| AirNet | CVPR'22 | 9M | 27.94/0.962 | 34.90/0.967 | 33.92/0.933 | 31.26/0.888 | 28.00/0.797 | 31.20/0.910 | contrastive-based & degradation-guided | |
| PIP_restormer | arXiv'23 | 27M | 32.09/0.981 | 38.29/0.984 | 34.24/0.936 | 31.60/0.893 | 28.35/0.806 | 32.91/0.920 | prompt-in-prompt learning | |
| IDR | CVPR'23 | 15M | 29.87/0.970 | 36.03/0.971 | 33.89/0.931 | 31.32/0.884 | 28.04/0.798 | 31.83/0.911 | ingredient-oriented learning | |
| PromptIR | NeurIPS'23 | 36M | 30.58/0.974 | 36.37/0.972 | 33.98/0.933 | 31.31/0.888 | 28.06/0.799 | 32.06/0.913 | prompt for AiOIR | |
| Gridformer | IJCV'23 | 34M | 30.37/0.970 | 37.15/0.972 | 33.93/0.931 | 31.37/0.887 | 28.11/0.801 | 32.19/0.912 | transformer with grid structure | |
| ProRes | arXiv'23 | 371M | 28.38/0.938 | 33.68/0.954 | 32.10/0.907 | 30.18/0.863 | 27.58/0.779 | 30.38/0.888 | degradation-aware visual prompt | |
| NDR | TIP'24 | 28M | 28.64/0.962 | 35.42/0.969 | 34.01/0.932 | 31.36/0.887 | 28.10/0.798 | 31.51/0.910 | neural degradation representation | |
| Art_PromptIR | ACM MM'24 | 36M | 30.83/0.979 | 37.94/0.982 | 34.06/0.934 | 31.42/0.891 | 28.14/0.801 | 32.49/0.917 | via multi-task collaboration | |
| AnyIR | arXiv'24 | 6M | 31.38/0.979 | 37.90/0.981 | 33.95/0.933 | 31.29/0.889 | 28.03/0.797 | 32.51/0.916 | local-global gated intertwining | |
| DaAIR | arXiv'24 | 6M | 32.30/0.981 | 37.10/0.978 | 33.92/0.930 | 31.26/0.884 | 28.00/0.792 | 32.51/0.913 | efficient degradation-aware | |
| MEASNet | arXiv'24 | 31M | 31.61/0.981 | 39.00/0.985 | 34.12/0.935 | 31.46/0.892 | 28.19/0.803 | 32.85/0.919 | multi-expert adaptive selection | |
| U-WADN | arXiv'24 | 6M | 29.21/0.971 | 35.36/0.968 | 33.73/0.931 | 31.14/0.886 | 27.92/0.793 | 31.47/0.910 | unified-width adaptive network | |
| Shi et al. | arXiv'24 | - | 29.20/0.972 | 37.50/0.980 | 34.59/0.941 | 31.83/0.900 | 28.46/0.814 | 32.32/0.921 | frequency-aware transformers | |
| DyNet | arXiv'24 | 16M | 31.98/0.981 | 38.71/0.983 | 34.11/0.936 | 31.44/0.892 | 28.18/0.803 | 32.88/0.920 | dynamic pre-training | |
| Hair | arXiv'24 | 29M | 30.98/0.979 | 38.59/0.983 | 34.16/0.935 | 31.51/0.892 | 28.24/0.803 | 32.70/0.919 | hypernetworks-based | |
| LoRA-IR | arXiv'24 | - | 30.68/0.961 | 37.75/0.979 | 34.06/0.935 | 31.42/0.891 | 28.18/0.803 | 32.42/0.914 | mixture of low-rank experts | |
| Up-Restorer | AAAI'25 | 28M | 30.68/0.977 | 36.74/0.978 | 33.99/0.933 | 31.33/0.888 | 28.07/0.799 | 32.16/0.915 | ADMM-solver-based design | |
| TUR_PromptIR | AAAI'25 | 36M | 31.17/0.978 | 38.57/0.984 | 34.06/0.932 | 31.40/0.887 | 28.13/0.797 | 32.67/0.916 | a novel loss function | |
| AdaIR | ICLR'25 | 29M | 31.06/0.980 | 38.64/0.983 | 34.12/0.935 | 31.45/0.892 | 28.19/0.802 | 32.69/0.918 | frequency mining and modulation | |
| DA-RCOT | TPAMI'25 | 50M | 31.26/0.977 | 38.36/0.983 | 33.98/0.934 | 31.33/0.890 | 28.10/0.801 | 32.60/0.917 | as an optimal transport problem | |
| ABAIR | arXiv'24 | 41M | 33.53/0.984 | 38.69/0.982 | 34.18/0.935 | 31.38/0.890 | 28.25/0.804 | 33.21/0.919 | a three-phase approach | |
| MoCE-IR | CVPR'25 | 25M | 31.34/0.979 | 38.57/0.984 | 34.11/0.932 | 31.45/0.888 | 28.18/0.800 | 32.73/0.917 | mixture-of-complexity-experts | |
| MTAIR | arXiv'24 | - | 31.34/0.983 | 39.15/0.984 | 34.14/0.936 | 31.50/0.893 | 28.24/0.805 | 32.87/0.920 | Mamba-Transformer cross-dimensional | |
| DCPT_PromptIR | ICLR'25 | 35M | 31.91/0.981 | 38.43/0.983 | 34.17/0.933 | 31.53/0.889 | 28.30/0.802 | 32.87/0.918 | learn to classify degradation | |
| RamIR | APPL INTELL'25 | 22M | 31.29/0.977 | 38.16/0.981 | 34.04/0.931 | 31.61/0.891 | 28.19/0.801 | 32.65/0.916 | prompt-driven Mamba-based | |
| Cat-AIR | arXiv'25 | - | 31.49/0.980 | 38.43/0.983 | 34.11/0.935 | 31.44/0.892 | 28.14/0.803 | 32.72/0.919 | content and task-aware framework | |
| DSwinIR | arXiv'25 | 24M | 31.86/0.980 | 37.73/0.983 | 34.12/0.933 | 31.59/0.890 | 28.31/0.803 | 32.72/0.917 | deformable sliding window transformer | |
| CPL_PromptIR | arXiv'25 | 36M | 31.27/0.980 | 38.77/0.985 | 34.15/0.933 | 31.50/0.889 | 28.23/0.800 | 32.78/0.917 | contrastive prompt regularization | |
| AnyIR | arXiv'25 | 6M | 31.75/0.981 | 38.61/0.984 | 34.12/0.936 | 31.46/0.893 | 28.20/0.804 | 32.83/0.920 | local-global gated mechanism | |
| MIRAGE | arXiv'25 | 10M | 31.86/0.981 | 38.94/0.985 | 34.12/0.935 | 31.46/0.891 | 28.19/0.803 | 32.91/0.919 | within the SPD manifold space | |
| BaryIR | arXiv'25 | - | 31.33/0.980 | 38.95/0.984 | 34.16/0.935 | 31.54/0.892 | 28.25/0.802 | 32.85/0.919 | in continuous barycenter space | |
| SIPL_PromptIR | arXiv'25 | 39M | 31.09/0.977 | 38.43/0.984 | 34.12/0.933 | 31.48/0.889 | 28.22/0.800 | 32.67/0.917 | privilege learning | |
| TextPromptIR | arXiv'23 | 124M+110M | 31.65/0.978 | 38.41/0.982 | 34.17/0.936 | 31.52/0.893 | 28.26/0.805 | 32.80/0.919 | textual prompt for AiOIR | |
| InstructIR-3D | ECCV'24 | 16M+17M | 30.22/0.959 | 37.98/0.978 | 34.15/0.933 | 31.52/0.890 | 28.30/0.804 | 32.43/0.913 | natural language prompts | |
| DA-CLIP_IR-SDE | ICLR'24 | 49M+125M | 26.83/0.962 | 35.85/0.972 | 31.43/0.889 | 25.05/0.605 | 18.33/0.308 | 27.50/0.747 | degradation-aware vision-language model | |
| Perceive-IR | arXiv'24 | 42M+86M | 30.87/0.975 | 38.29/0.980 | 34.13/0.934 | 31.42/0.888 | 28.09/0.797 | 32.56/0.915 | prompt-based foundation model | |
| VLU-Net | CVPR'25 | 35M+88M | 30.71/0.980 | 38.93/0.984 | 34.13/0.935 | 31.48/0.892 | 28.23/0.804 | 32.70/0.919 | vision-language gradient descent-driven | |
| DFPIR | CVPR'25 | 31M+63M | 31.87/0.980 | 38.65/0.982 | 34.14/0.935 | 31.47/0.893 | 28.25/0.806 | 32.88/0.919 | degradation-aware feature perturbation |
| Type | Method | Venue & Year | Params | Dehazing (SOTS) | Deraining (Rain100L) | Denoising (BSD68) | Deblurring (Gopro) | Low-light (LOL) | Average | Approach |
|---|---|---|---|---|---|---|---|---|---|---|
| Single | ADFNet | AAAI'23 | 8M | 24.18 / 0.928 | 32.97 / 0.943 | 31.15 / 0.882 | 25.79 / 0.781 | 21.15 / 0.823 | 27.05 / 0.871 | Specific |
| DehazeFormer | TIP'23 | 25M | 25.31 / 0.937 | 33.68 / 0.954 | 30.89 / 0.880 | 25.93 / 0.785 | 21.31 / 0.819 | 27.42 / 0.875 | Specific | |
| DRSformer | CVPR'23 | 34M | 24.66 / 0.931 | 33.45 / 0.953 | 30.97 / 0.881 | 25.56 / 0.780 | 21.77 / 0.821 | 27.28 / 0.873 | Specific | |
| HI-Diff | NeurIPS'23 | 24M | 25.09 / 0.935 | 33.26 / 0.951 | 30.61 / 0.878 | 26.48 / 0.800 | 22.01 / 0.870 | 27.49 / 0.887 | Specific | |
| Retinexformer | ICCV'23 | 2M | 24.81 / 0.933 | 32.68 / 0.940 | 30.84 / 0.880 | 25.09 / 0.779 | 22.76 / 0.863 | 27.24 / 0.873 | Specific | |
| Multiple | SwinIR | ICCVW'21 | 1M | 21.50 / 0.891 | 30.78 / 0.923 | 30.59 / 0.868 | 24.52 / 0.773 | 17.81 / 0.723 | 25.04 / 0.835 | General |
| MIRNet-v2 | TPAMI'22 | 6M | 24.03 / 0.927 | 33.89 / 0.954 | 30.97 / 0.881 | 26.30 / 0.799 | 21.52 / 0.815 | 27.34 / 0.875 | General | |
| DGUNet | CVPR'22 | 17M | 24.78 / 0.940 | 36.62 / 0.971 | 31.10 / 0.883 | 27.25 / 0.837 | 21.87 / 0.823 | 28.32 / 0.891 | General | |
| Restormer | CVPR'22 | 26M | 24.09 / 0.927 | 34.81 / 0.960 | 31.49 / 0.884 | 27.22 / 0.829 | 20.41 / 0.806 | 27.60 / 0.881 | General | |
| NAFNet | ECCV'22 | 17M | 25.23 / 0.939 | 35.56 / 0.967 | 31.02 / 0.883 | 26.53 / 0.808 | 20.49 / 0.809 | 27.76 / 0.881 | General | |
| FSNet | TPAMI'23 | 13M | 25.53 / 0.943 | 36.07 / 0.968 | 31.33 / 0.883 | 28.32 / 0.869 | 22.29 / 0.829 | 28.71 / 0.898 | General | |
| MambaIR | ECCV'24 | 27M | 25.81 / 0.944 | 36.55 / 0.971 | 31.41 / 0.884 | 28.61 / 0.875 | 22.49 / 0.832 | 28.97 / 0.901 | General | |
| All-in-One | DL | TPAMI'19 | 2M | 20.54 / 0.826 | 21.96 / 0.762 | 23.09 / 0.745 | 19.86 / 0.672 | 19.83 / 0.712 | 21.05 / 0.743 | parameterized image operator |
| Transweather | CVPR'22 | 38M | 21.32 / 0.885 | 29.43 / 0.905 | 29.00 / 0.841 | 25.12 / 0.757 | 21.21 / 0.792 | 25.22 / 0.836 | weather type queries | |
| TAPE | ECCV'22 | 1M | 22.16 / 0.861 | 29.67 / 0.904 | 30.18 / 0.855 | 24.47 / 0.763 | 18.97 / 0.621 | 25.09 / 0.801 | task-agnostic prior | |
| AirNet | CVPR'22 | 9M | 21.04 / 0.884 | 32.98 / 0.951 | 30.91 / 0.882 | 24.35 / 0.781 | 18.18 / 0.735 | 25.49 / 0.846 | contrastive-based & degradation-guided | |
| IDR | CVPR'23 | 15M | 25.24 / 0.943 | 35.63 / 0.965 | 31.60 / 0.887 | 27.87 / 0.846 | 21.34 / 0.826 | 28.34 / 0.893 | ingredient-oriented learning | |
| PIP_Restormer | arXiv'23 | 27M | 32.11 / 0.979 | 38.09 / 0.983 | 30.94 / 0.877 | 28.61 / 0.861 | 24.06 / 0.859 | 30.81 / 0.901 | prompt-in-prompt learning | |
| PromptIR | NeurIPS'23 | 36M | 26.54 / 0.949 | 36.37 / 0.970 | 31.47 / 0.886 | 28.71 / 0.881 | 22.68 / 0.832 | 29.15 / 0.904 | prompt for AiOIR | |
| DASL_MPRNet | arXiv'23 | 15M | 25.82 / 0.947 | 38.02 / 0.980 | 31.57 / 0.890 | 26.91 / 0.823 | 20.96 / 0.826 | 28.66 / 0.893 | decomposition ascribed synergistic | |
| Gridformer | IJCV'23 | 34M | 26.79 / 0.951 | 36.61 / 0.971 | 31.45 / 0.885 | 29.22 / 0.884 | 22.59 / 0.831 | 29.33 / 0.904 | transformer with grid structure | |
| Art_PromptIR | ACM MM'24 | 36M | 29.93 / 0.908 | 22.09 / 0.891 | 29.43 / 0.843 | 25.61 / 0.776 | 21.99 / 0.811 | 25.81 / 0.846 | via multi-task collaboration | |
| DaAIR | arXiv'24 | 6M | 31.97 / 0.980 | 36.28 / 0.975 | 31.07 / 0.878 | 29.51 / 0.890 | 22.38 / 0.825 | 30.24 / 0.910 | efficient degradation-aware | |
| AnyIR | arXiv'24 | 6M | 29.84 / 0.977 | 36.91 / 0.977 | 31.15 / 0.882 | 26.86 / 0.822 | 23.50 / 0.845 | 29.65 / 0.901 | local-global gated intertwining | |
| MEASNet | arXiv'24 | 31M | 31.05 / 0.980 | 38.32 / 0.982 | 31.40 / 0.888 | 29.41 / 0.890 | 23.00 / 0.845 | 30.64 / 0.917 | multi-expert adaptive selection | |
| Hair | arXiv'24 | 29M | 30.62 / 0.978 | 38.11 / 0.981 | 31.49 / 0.891 | 28.52 / 0.874 | 23.12 / 0.847 | 30.37 / 0.914 | hypernetworks-based | |
| TUR_Transweather | AAAI'25 | 38M | 29.68 / 0.966 | 33.09 / 0.952 | 30.40 / 0.869 | 26.63 / 0.815 | 23.02 / 0.838 | 28.56 / 0.888 | a novel loss function | |
| AdaIR | ICLR'25 | 29M | 30.53 / 0.978 | 38.02 / 0.981 | 31.35 / 0.889 | 28.12 / 0.858 | 23.00 / 0.845 | 30.20 / 0.910 | frequency mining and modulation | |
| DA-RCOT | TPAMI'25 | 50M | 30.96 / 0.975 | 37.87 / 0.980 | 31.23 / 0.888 | 28.68 / 0.872 | 23.25 / 0.836 | 30.40 / 0.911 | as an optimal transport problem | |
| ABAIR | arXiv'24 | 41M | 33.46 / 0.983 | 38.18 / 0.983 | 31.38 / 0.898 | 29.00 / 0.878 | 24.20 / 0.865 | 31.24 / 0.921 | a three-phase approach | |
| MoCE-IR | CVPR'25 | 25M | 30.48 / 0.974 | 38.04 / 0.982 | 31.34 / 0.887 | 30.05 / 0.899 | 23.00 / 0.852 | 30.58 / 0.919 | mixture-of-complexity-experts framework | |
| DCPT_PromptIR | ICLR'25 | 35M | 30.72 / 0.977 | 37.32 / 0.978 | 31.32 / 0.885 | 28.84 / 0.877 | 23.35 / 0.840 | 30.31 / 0.911 | learn to classify degradation | |
| RamIR | APPL INTELL'25 | 22M | 31.09 / 0.979 | 37.56 / 0.979 | 31.44 / 0.886 | 28.82 / 0.878 | 22.02 / 0.828 | 30.18 / 0.910 | prompt-driven Mamba-based | |
| D3Net | arXiv'25 | 38M | 32.57 / 0.965 | 38.35 / 0.973 | 31.73 / 0.860 | 32.70 / 0.851 | 26.49 / 0.857 | 32.36 / 0.901 | cross-domain and dynamic decomposition | |
| Cat-AIR | arXiv'25 | - | 30.88 / 0.978 | 38.21 / 0.982 | 31.38 / 0.890 | 28.91 / 0.876 | 23.46 / 0.848 | 30.57 / 0.915 | content and task-aware framework | |
| DSwinIR | arXiv'25 | 24M | 30.09 / 0.975 | 37.77 / 0.982 | 31.34 / 0.885 | 29.17 / 0.879 | 22.64 / 0.843 | 30.19 / 0.913 | deformable sliding window transformer | |
| CPL_PromptIR | arXiv'25 | 36M | 30.82 / 0.978 | 38.20 / 0.983 | 31.41 / 0.887 | 28.72 / 0.874 | 23.65 / 0.855 | 30.55 / 0.915 | contrastive prompt regularization | |
| AnyIR | arXiv'25 | 6M | 31.41 / 0.980 | 38.51 / 0.983 | 31.37 / 0.891 | 28.85 / 0.883 | 23.11 / 0.856 | 30.65 / 0.919 | local-global gated mechanism | |
| MIRAGE | arXiv'25 | 10M | 31.45 / 0.980 | 38.92 / 0.985 | 31.41 / 0.892 | 28.10 / 0.858 | 23.59 / 0.858 | 30.68 / 0.914 | within the SPD manifold space | |
| BaryIR | arXiv'25 | - | 31.12 / 0.976 | 38.05 / 0.981 | 31.43 / 0.891 | 29.30 / 0.888 | 23.38 / 0.852 | 30.66 / 0.918 | in continuous barycenter space | |
| InstructIR-5D | ECCV'24 | 16M+17M | 36.84 / 0.973 | 27.10 / 0.956 | 31.40 / 0.887 | 29.40 / 0.886 | 23.00 / 0.836 | 29.55 / 0.907 | natural language prompts | |
| Perceive-IR | TIP'25 | 42M+86M | 28.19 / 0.964 | 37.25 / 0.977 | 31.44 / 0.887 | 29.46 / 0.886 | 22.88 / 0.833 | 29.84 / 0.909 | quality-aware degradation | |
| VLU-Net | CVPR'25 | 35M+88M | 30.84 / 0.980 | 38.54 / 0.982 | 31.43 / 0.891 | 27.46 / 0.840 | 22.29 / 0.833 | 30.11 / 0.905 | vision-language gradient descent-driven | |
| DFPIR | CVPR'25 | 31M+63M | 31.64 / 0.979 | 37.62 / 0.978 | 31.29 / 0.889 | 28.82 / 0.873 | 23.62 / 0.852 | 30.60 / 0.914 | degradation-aware feature perturbation | |
| CyclicPrompt | arXiv'25 | 30M+486M | 33.02/0.983 | 37.03/0.989 | 31.33/0.941 | 29.42/0.925 | 21.79/0.837 | 30.52/0.935 | cyclic prompt-driven universal framework |
| Method | Snow | Rain+Fog | Raindrop | Average | Params |
|---|---|---|---|---|---|
| SwinIR | 28.18/0.880 | 23.23/0.869 | 30.82/0.904 | 27.41/0.884 | 12M |
| MPRNet | 28.66/0.869 | 30.25/0.914 | 30.99/0.916 | 29.30/0.900 | 16M |
| Restormer | 29.37/0.881 | 29.22/0.907 | 31.21/0.919 | 29.93/0.902 | 26M |
| All-in-One | 28.33/0.882 | 24.71/0.898 | 31.12/0.927 | 28.05/0.902 | 44M |
| Transweather | 29.31/0.888 | 28.83/0.900 | 30.17/0.916 | 29.44/0.901 | 38M |
| AirNet | 27.92/0.858 | 23.12/0.837 | 28.23/0.892 | 26.42/0.862 | 9M |
| WGWS-Net | 28.91/0.856 | 29.28/0.922 | 32.01/0.925 | 30.07/0.901 | 6M |
| WeatherDiff | 30.09/0.904 | 29.64/0.931 | 30.71/0.931 | 30.15/0.922 | 83M |
| TKMANet | 30.24/0.902 | 29.92/0.917 | 30.99/0.927 | 30.38/0.915 | 29M |
| UtilityIR | 29.47/0.879 | 31.16/0.927 | 32.01/0.925 | 30.88/0.910 | 26M |
| AWRCP | 31.92/0.934 | 31.39/0.933 | 31.93/0.931 | 31.75/0.933 | - |
| Histoformer | 32.16/0.926 | 32.08/0.939 | 33.06/0.944 | 32.43/0.936 | 17M |
| LoRA-IR | 32.28/0.930 | 32.62/0.945 | 33.39/0.949 | 32.76/0.941 | - |
| T3-DiffWeather | 32.37/0.936 | 31.99/0.937 | 32.66/0.941 | 32.34/0.938 | 69M+25M |
| MWFormer | 30.92/0.908 | 30.27/0.912 | 31.91/0.927 | 31.03/0.916 | 170M |
| TUR_Transweather | 30.62/0.909 | 29.75/0.907 | 31.61/0.933 | 30.66/0.916 | 38M |
| CyclicPrompt | 32.16/0.927 | 32.81/0.937 | 32.57/0.945 | 32.51/0.936 | 30M+486M |
| DSwinIR | 32.58/0.931 | 32.76/0.950 | 32.88/0.947 | 32.74/0.943 | 24M |
| DA2Diff | 31.42/0.916 | 31.58/0.939 | 33.01/0.945 | 31.67/0.933 | - |
| HOGformer | 32.41/0.930 | 32.89/0.946 | 32.72/0.945 | 32.67/0.940 | 17M |
| CPL_PromptIR | 32.27/0.928 | 32.16/0.942 | 32.73/0.943 | 32.39/0.938 | 36M |
| MODEM | 32.52/0.929 | 33.10/0.941 | 33.01/0.943 | 32.87/0.938 | 20M |
| Method | Haze | Rain | Snow | Average | Params |
|---|---|---|---|---|---|
| the WeatherStream Dataset | |||||
| NAFNet | 22.20/0.803 | 23.01/0.803 | 22.11/0.826 | 22.44/0.811 | 17M |
| GRL | 22.88/0.802 | 23.75/0.805 | 22.59/0.829 | 23.07/0.812 | 3M |
| Restormer | 22.90/0.803 | 23.67/0.804 | 22.51/0.828 | 22.86/0.812 | 26M |
| MPRNet | 21.73/0.763 | 21.50/0.791 | 20.74/0.801 | 21.32/0.785 | 16M |
| Transweather | 22.55/0.774 | 22.21/0.772 | 21.79/0.792 | 22.18/0.779 | 38M |
| AirNet | 21.56/0.770 | 22.52/0.797 | 21.44/0.812 | 21.84/0.793 | 9M |
| TKMANet | 22.38/0.805 | 23.22/0.795 | 22.25/0.827 | 22.62/0.809 | 29M |
| WGWS-Net | 22.78/0.800 | 23.80/0.807 | 22.72/0.831 | 23.10/0.813 | 6M |
| the Real-World Dataset | |||||
| TKMANet | 20.10/0.85 | 37.32/0.97 | 29.37/0.88 | 28.93/0.90 | 29M |
| TransWeather | 17.33/0.82 | 33.64/0.93 | 26.92/0.86 | 26.71/0.86 | 38M |
| TUR_Transweather | 20.38/0.88 | 39.78/0.98 | 29.72/0.91 | 29.96/0.92 | 38M |
| WGWS-Net | 29.46/0.85 | 38.94/0.98 | 33.61/0.93 | 34.01/0.92 | 6M |
| DSwinIR | 30.14/0.89 | 40.60/0.98 | 33.80/0.93 | 34.85/0.93 | 24M |
| Type | Method | Venue & Year | PSNR | SSIM | Params |
|---|---|---|---|---|---|
| One-to-One | MPRNet | CVPR'21 | 25.47 | 0.856 | 16M |
| One-to-One | MIRNetv2 | TPAMI'22 | 25.37 | 0.854 | 6M |
| One-to-One | Restormer | CVPR'22 | 26.99 | 0.865 | 26M |
| One-to-One | DGUNet | CVPR'22 | 25.33 | 0.844 | 18M |
| One-to-One | NAFNet | ECCV'22 | 26.22 | 0.796 | 17M |
| One-to-One | SRUDC | ICCV'23 | 27.64 | 0.860 | 7M |
| One-to-One | Fourmer | ICML'23 | 23.44 | 0.789 | 6M |
| One-to-One | OKNet | AAAI'24 | 26.33 | 0.861 | 6M |
| One-to-Many | AirNet | CVPR'22 | 23.75 | 0.814 | 9M |
| One-to-Many | TransWeather | CVPR'22 | 23.13 | 0.804 | 38M |
| One-to-Many | WeatherDiff | TPAMI'23 | 24.09 | 0.799 | 83M |
| One-to-Many | PromptIR | NeurIPS'23 | 25.90 | 0.850 | 36M |
| One-to-Many | WGWS-Net | CVPR'23 | 26.96 | 0.861 | 6M |
| One-to-Many | HAIR | arXiv'24 | 27.85 | 0.866 | 29M |
| One-to-Composite | OneRestore | ECCV'24 | 28.47 | 0.878 | 6M |
| One-to-Composite | AllRestorer | arXiv'24 | 33.72 | 0.944 | 12M+88M |
| One-to-Composite | DA-CLIP_NAFNet | ICLR'24 | 26.56 | 0.860 | 86M+125M |
| One-to-Composite | MoCE-IR-S | CVPR'25 | 29.05 | 0.881 | 11M |
| One-to-Composite | DCPT_NAFNet | ICLR'25 | 30.66 | 0.891 | 68M |
| One-to-Composite | VL-UR | arXiv'25 | 28.76 | 0.879 | - |
| One-to-Composite | MIRAGE | arXiv'25 | 29.33 | 0.887 | 10M |
| Step-by-Step | CoR_HAIR | arXiv'24 | 28.33 | 0.869 | 30M |
| Step-by-Step | CoR_OneRestore | arXiv'24 | 28.84 | 0.879 | 8M |
| Methods | Blur | Noise | JPEG | Haze | Rain | Raindrop | Lowlight | B+N | B+J | N+J | R+H | L+H | L+R | L+B | L+N | L+J | L+B+N | L+B+J | L+N+J | B+N+J | Average |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Input | 20.61/0.6544 | 37.30/0.9329 | 32.83/0.9530 | 13.47/0.6394 | 23.45/0.7791 | 20.76/0.6792 | 6.99/0.1802 | 22.85/0.7559 | 22.87/0.7450 | 38.45/0.9747 | 12.16/0.4774 | 9.94/0.3250 | 30.00/0.8935 | 10.55/0.4328 | 8.07/0.2535 | 8.50/0.3261 | 9.70/0.4079 | 9.01/0.4555 | 8.65/0.4984 | 22.44/0.7009 | 18.40/0.6033 |
| DGUNet | 21.86/0.7334 | 36.69/0.9494 | 29.80/0.8906 | 18.58/0.6762 | 25.47/0.8245 | 24.83/0.7924 | 16.16/0.6494 | 22.90/0.7633 | 22.86/0.7358 | 35.28/0.9564 | 18.79/0.4981 | 13.20/0.4182 | 30.66/0.9097 | 11.54/0.4922 | 12.56/0.5349 | 12.17/0.5433 | 10.60/0.4608 | 9.57/0.4897 | 9.64/0.5456 | 22.43/0.6945 | 20.27/0.6779 |
| Restormer | 21.81/0.7268 | 34.80/0.9311 | 29.08/0.8742 | 13.53/0.5316 | 24.70/0.7773 | 23.87/0.7395 | 8.64/0.3845 | 22.83/0.7474 | 22.76/0.7266 | 34.09/0.9412 | 12.88/0.4096 | 10.84/0.3110 | 29.72/0.8757 | 10.90/0.4487 | 8.40/0.3255 | 10.08/0.4492 | 9.74/0.4110 | 9.30/0.4647 | 10.30/0.5761 | 22.28/0.6836 | 18.52/0.6167 |
| DiffIR | 21.88/0.7346 | 37.69/0.9508 | 32.94/0.9313 | 19.06/0.7801 | 25.77/0.8249 | 25.34/0.8183 | 16.90/0.7311 | 22.90/0.7502 | 22.91/0.7373 | 38.97/0.9732 | 19.63/0.6275 | 18.78/0.7435 | 30.87/0.8971 | 22.10/0.7203 | 14.81/0.4651 | 25.66/0.8761 | 22.00/0.6862 | 21.68/0.7303 | 31.36/0.9613 | 22.48/0.6958 | 24.68/0.7817 |
| IR-SDE | 19.08/0.6395 | 23.67/0.8610 | 21.91/0.7738 | 13.67/0.5080 | 20.05/0.6541 | 20.61/0.6789 | 6.93/0.1578 | 19.77/0.6610 | 18.82/0.6118 | 22.35/0.8187 | 12.62/0.3905 | 9.64/0.2664 | 25.49/0.7651 | 10.21/0.3399 | 7.95/0.2049 | 8.44/0.2649 | 9.29/0.3274 | 8.63/0.3729 | 8.29/0.4027 | 18.64/0.5838 | 15.30/0.5141 |
| RDDM | 16.37/0.7183 | 14.94/0.8519 | 17.95/0.8419 | 11.12/0.6435 | 15.55/0.7118 | 15.47/0.6528 | 13.16/0.7027 | 15.82/0.7156 | 16.75/0.6822 | 18.23/0.9066 | 10.52/0.4568 | 13.87/0.3972 | 12.53/0.5537 | 17.26/0.6471 | 13.39/0.6025 | 14.09/0.5931 | 17.34/0.6457 | 15.98/0.6822 | 16.88/0.8222 | 17.76/0.6679 | 15.24/0.6747 |
| X-Restormer | 21.88/0.7325 | 35.86/0.9412 | 28.58/0.8646 | 16.47/0.6257 | 25.70/0.8076 | 26.11/0.8368 | 16.02/0.6404 | 22.90/0.7544 | 22.85/0.7321 | 33.83/0.9405 | 15.17/0.4354 | 14.75/0.4416 | 30.93/0.9026 | 19.38/0.6605 | 9.75/0.3866 | 18.66/0.7205 | 22.00/0.6841 | 13.63/0.5738 | 16.40/0.7810 | 22.41/0.6894 | 21.66/0.7075 |
| All-in-One models | |||||||||||||||||||||
| AirNet | 18.07/0.5950 | 19.61/0.7588 | 19.42/0.5495 | 14.13/0.3792 | 17.61/0.4446 | 16.97/0.3233 | 6.60/0.1153 | 18.41/0.6054 | 19.22/0.6108 | 17.81/0.5239 | 13.53/0.2376 | 8.39/0.1452 | 22.41/0.6397 | 9.84/0.3293 | 7.75/0.2162 | 8.02/0.2210 | 8.76/0.2676 | 7.99/0.3122 | 7.83/0.3065 | 18.32/0.5282 | 14.03/0.4054 |
| TransWeather | 21.34/0.7103 | 30.12/0.8945 | 23.52/0.7296 | 17.72/0.5635 | 23.49/0.6886 | 22.94/0.6814 | 14.95/0.6295 | 22.19/0.7363 | 22.14/0.7105 | 25.59/0.8377 | 18.43/0.4245 | 15.99/0.4285 | 28.29/0.8663 | 21.35/0.6764 | 11.56/0.5181 | 19.16/0.6528 | 20.83/0.6607 | 19.64/0.6918 | 21.41/0.8068 | 21.64/0.6577 | 21.11/0.6782 |
| IDR | 17.75/0.6139 | 21.51/0.7896 | 17.96/0.5207 | 12.60/0.3581 | 16.37/0.4363 | 16.21/0.3422 | 6.96/0.1837 | 18.05/0.6243 | 18.21/0.6063 | 16.05/0.4826 | 11.40/0.2410 | 9.77/0.2377 | 23.42/0.7939 | 10.34/0.3976 | 7.92/0.2631 | 8.32/0.2696 | 9.51/0.3652 | 8.89/0.4170 | 8.34/0.3500 | 17.16/0.5209 | 13.83/0.4406 |
| PromptIR | 21.91/0.7339 | 36.57/0.9478 | 29.63/0.8842 | 15.36/0.4982 | 27.59/0.8411 | 25.83/0.8327 | 16.33/0.6353 | 22.93/0.7587 | 22.90/0.7360 | 35.08/0.9524 | 16.61/0.4985 | 15.36/0.4982 | 31.03/0.9039 | 20.29/0.6723 | 10.83/0.4499 | 21.52/0.7671 | 22.63/0.6924 | 12.40/0.5597 | 22.71/0.8616 | 22.47/0.6942 | 22.49/0.7209 |
| DiffUIR | 25.31/0.7979 | 34.48/0.9622 | 30.09/0.9390 | 19.97/0.8193 | 30.17/0.9350 | 26.98/0.8908 | 14.02/0.7101 | 24.44/0.8039 | 21.36/0.6915 | 31.96/0.9782 | 20.24/0.7268 | 19.31/0.7745 | 32.35/0.9255 | 18.97/0.6939 | 13.88/0.6262 | 19.98/0.8706 | 18.89/0.6705 | 18.64/0.6751 | 20.66/0.9338 | 23.72/0.7861 | 23.27/0.8105 |
| DA-CLIP | 20.92/0.6954 | 29.45/0.9027 | 25.77/0.7816 | 15.91/0.5399 | 23.04/0.6849 | 20.86/0.6490 | 17.34/0.7412 | 22.15/0.7293 | 21.36/0.6940 | 26.03/0.8602 | 13.97/0.3563 | 12.62/0.3220 | 21.58/0.7246 | 16.17/0.6241 | 15.70/0.6365 | 15.86/0.6048 | 15.30/0.6202 | 12.46/0.5774 | 15.34/0.7068 | 21.19/0.6589 | 19.15/0.6554 |
| Real-ESRGAN | 25.20/0.7868 | 34.46/0.9585 | 27.62/0.9108 | 22.07/0.8380 | 28.95/0.9226 | 28.94/0.9115 | 19.26/0.8709 | 23.48/0.7728 | 20.41/0.6562 | 29.71/0.9566 | 20.40/0.7153 | 21.79/0.8084 | 32.16/0.9116 | 21.95/0.7254 | 17.49/0.7358 | 22.89/0.8704 | 21.43/0.6612 | 19.49/0.6582 | 26.11/0.9496 | 21.40/0.7197 | 24.26/0.8173 |
| SUPIR | 20.92/0.6649 | 34.11/0.9035 | 24.52/0.7189 | - | - | - | - | 21.69/0.6828 | 21.34/0.6293 | 26.97/0.8003 | - | - | - | - | - | - | - | - | - | 20.18/0.5768 | - |
| InstructIR | 20.15/0.6801 | 38.58/0.9628 | 33.44/0.9378 | 16.85/0.7555 | 30.18/0.8997 | 21.05/0.6828 | 20.04/0.8542 | 21.70/0.7185 | 21.39/0.6814 | 39.90/0.9770 | 13.49/0.5535 | 13.52/0.4983 | 29.87/0.8866 | 17.43/0.6676 | 16.37/0.4625 | 18.06/0.7787 | 12.78/0.5392 | 17.39/0.7092 | 19.13/0.9105 | 22.42/0.6980 | 22.18/0.7426 |
| AutoDIR | 20.31/0.6946 | 36.84/0.9221 | 32.99/0.9280 | 15.23/0.6264 | 25.69/0.7711 | 20.82/0.6687 | 21.90/0.8288 | 21.90/0.7293 | 22.03/0.7088 | 37.55/0.9616 | 14.90/0.4213 | 14.50/0.4456 | 27.15/0.8576 | 19.14/0.6570 | 17.49/0.6900 | 16.14/0.6375 | 18.91/0.6499 | 16.91/0.6761 | 18.78/0.8122 | 22.37/0.6876 | 22.07/0.7187 |
| FoundIR | 25.93/0.7914 | 38.98/0.9647 | 34.03/0.9427 | 22.19/0.8492 | 34.29/0.9434 | 29.74/0.9160 | 18.98/0.8576 | 23.87/0.7737 | 29.58/0.8544 | 40.43/0.9795 | 21.80/0.7582 | 22.82/0.8048 | 33.96/0.9330 | 24.15/0.7808 | 18.95/0.7454 | 29.08/0.9098 | 23.56/0.7550 | 22.55/0.8181 | 33.46/0.9636 | 27.98/0.7452 | 27.81/0.8529 |
If our survey helps your research or work, please consider citing our paper. The following are BibTeX references:
@article{jiang2025survey,
title={A survey on all-in-one image restoration: Taxonomy, evaluation and future trends},
author={Jiang, Junjun and Zuo, Zengyuan and Wu, Gang and Jiang, Kui and Liu, Xianming},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume={47},
number={12},
pages={11892--11911},
year={2025}
}
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