Efficient Degradation-agnostic Image Restoration via Channel-Wise Functional Decomposition and Manifold Regularization
Mohamed bin Zayed University of Artificial Intelligence（莫扎德·本·泽德人工智能大学）；University of Trento（特伦托大学）；ETH Zürich（苏黎世联邦理工学院）；HKUST (GZ)（香港科技大学（广州））；Peking University（北京大学）；University of California, Merced（加州大学默塞德分校）
Bin Ren, Yawei Li, Xu Zheng, Yuqian Fu, Danda Pani Paudel, Hong Liu, Ming-Hsuan Yang, Luc Van Gool, Nicu Sebe
备注：
Accepted by ICLR'2026, All-in-One Image Restoration, low-level vision, Transformer
Degradation-agnostic image restoration aims to handle diverse corruptions with one unified model, but faces fundamental challenges in balancing efficiency and performance across different degradation types. Existing approaches either sacrifice efficiency for versatility or fail to capture the distinct representational requirements of various degradations. We present MIRAGE, an efficient framework that addresses these challenges through two key innovations. First, we propose a channel-wise functional decomposition that systematically repurposes channel redundancy in attention mechanisms by assigning CNN, attention, and MLP branches to handle local textures, global context, and channel statistics, respectively. This principled decomposition enables degradation-agnostic learning while achieving superior efficiency-performance trade-offs. Second, we introduce manifold regularization that performs cross-layer contrastive alignment in Symmetric Positive Definite (SPD) space, which empirically improves feature consistency and generalization across degradation types. Extensive experiments demonstrate that MIRAGE achieves state-of-the-art performance with remarkable efficiency, outperforming existing methods in various all-in-one IR settings while offering a scalable and generalizable solution for challenging unseen IR scenarios.
Tokyo Institute of Technology（东京工业大学）；Kadinche Corporation（Kadinche公司）；National Institute of Advanced Industrial Science and Technology（国立先进工业科学技术研究所）
Image restoration from a single image degradation type, such as blurring, hazing, random noise, and compression has been investigated for decades. However, image degradations in practice are often a mixture of several types of degradation. Such compositional degradations complicate restoration because they require the differentiation of different degradation types and levels. In this paper, we propose a convolutional neural network (CNN) model for estimating the degradation properties of a given degraded image. Furthermore, we introduce an image restoration CNN model that adopts the estimated …
The deep-learning-based image restoration and fusion methods have achieved remarkable results. However, the existing restoration and fusion methods paid little research attention to the robustness problem caused by dynamic degradation. In this paper, we propose a novel dynamic image restoration and fusion neural network, termed as DDRF-Net, which is capable of solving two problems, i.e., static restoration and fusion, dynamic degradation. In order to solve the static fusion problem of existing methods, dynamic convolution is introduced to learn dynamic restoration and fusion weights. In additi…
DVANet: Degradation-aware Visual-prior Alignment Network for Image Restoration
School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）；Shenzhen Research Institute of Northwestern Polytechnical University（西北工业大学深圳研究院）；State Key Laboratory of Internet of Things for Smart City, University of Macau（澳门大学智慧城市物联网国家重点实验室）
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University of Advanced Technology（深圳理工大学）
DPMambaIR: All-in-One Image Restoration via Degradation-Aware Prompt State Space Model
School of Information Engineering, Chang’an University（信息工程学院，长安大学）；School of Electronics and Information, Northwestern Polytechnical University（电子与信息学院，西北工业大学）
Degradation Frequency Curve: An Explicit Frequency-Quantified Representation for All-in-One Image Restoration
College of Electronic Science, National University of Defense Technology（国防科技大学电子科学学院）；College of Electronic Engineering, Beihang University（北航电子工程学院）；VCIP, School of Computer Science, Nankai University（南开大学计算机科学学院）
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA（多模态人工智能系统国家重点实验室（MAIS）、中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Ant Group（蚂蚁集团）；PeopleAI Inc.（PeopleAI公司）
Tokyo Institute of Technology（东京工业大学）；National Institute of Advanced Industrial Science and Technology（日本产业技术综合研究所）
Blind image restoration processors based on convolutional neural network (CNN) are intensively researched because of their high performance. However, they are too sensitive to the perturbation of the degradation model. They easily fail to restore the image whose degradation model is slightly different from the trained degradation model. In this paper, we propose a non-blind CNN-based image restoration processor, aiming to be robust against a perturbation of the degradation model compared to the blind restoration processor. Experimental comparisons demonstrate that the proposed non-blind CNN-ba…
ClusIR: Towards Cluster-Guided All-in-One Image Restoration
Zhongnan University of Economics and Law（中南财经政法大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Yunnan University（云南大学）；Nanyang Technological University（南洋理工大学）；Wuhan University（武汉大学）
University of Science and Technology of China（中国科学技术大学）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；Huawei Cloud BU（华为云业务单元）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；University of Macau（澳门大学）；Shenzhen Institutes of Advanced Technology, CAS（中国科学院深圳先进技术研究院）
Learning a generalized prior for natural image restoration is an important yet challenging task. Early methods mostly involved handcrafted priors including normalized sparsity, l_0 gradients, dark channel priors, etc. Recently, deep neural networks have been used to learn various image priors but do not guarantee to generalize. In this paper, we propose a novel approach that embeds a task-agnostic prior into a transformer. Our approach, named Task-Agnostic Prior Embedding (TAPE), consists of two stages, namely, task-agnostic pre-training and task-specific fine-tuning, where the first stage emb…
TIDE: Two-Stage Inverse Degradation Estimation with Guided Prior Disentanglement for Underwater Image Restoration
Mohamed bin Zayed University of AI（莫扎德·本·泽亚德人工智能大学）；University of Amsterdam（阿姆斯特丹大学）；University of Massachusetts, Amherst（马萨诸塞大学阿默斯特分校）；Nagasaki University（长崎大学）
Southwest Jiaotong University（西南交通大学）；Nanyang Technological University（南洋理工大学）；Tongji University（同济大学）；National University of Singapore（新加坡国立大学）；Tsinghua University（清华大学）
M2IR: Proactive All-in-One Image Restoration via Mamba-style Modulation and Mixture-of-Experts
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；School of Computer Science and Technology, Anhui University of Technology（安徽工业大学计算机科学与技术学院）；Shenzhen Ubiquitous Data Enabling Key Laboratory, Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院深圳 ubiquitous 数据赋能重点实验室）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）
Many image restoration techniques are highly dependent on the degradation used during training, and their performance declines significantly when applied to slightly different input. Blind and universal techniques attempt to mitigate this by producing a trained model that can adapt to varying conditions. However, blind techniques to date require prior knowledge of the degradation process, and assumptions regarding its parameter-space. In this paper we present the Self-Normalization Side-Chain (SCNC), a novel approach to blind universal restoration in which no prior knowledge of the degradation…
Bridging Degradation Discrimination and Generation for Universal Image Restoration
Institute of Medical Technology, Peking University（北京大学医学技术学院）；Biomedical Engineering Department, College of Future Technology, Peking University（北京大学未来技术学院生物医学工程系）；National Biomedical Imaging Center, Peking University（北京大学国家生物医学成像中心）；China Mobile Research Institute（中国移动研究院）
PhyDAE: Physics-Guided Degradation-Adaptive Experts for All-in-One Remote Sensing Image Restoration
School of Electronic and Information Engineering, Harbin Institute of Technology（电子与信息工程学院，哈尔滨工业大学）；Heilongjiang Province Key Laboratory of Space-Air-Ground Integrated Intelligent Remote Sensing（黑龙江省空间-空天地一体化智能遥感重点实验室）
Universal Image Restoration Pre-training via Degradation Classification
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究院）；College of Future Technology, Peking University（北京大学未来技术学院）；National Biomedical Imaging Center, Peking University（北京大学生物医学成像中心）；China Mobile Research Institute（中国移动研究院）
Beyond Degradation Redundancy: Contrastive Prompt Learning for All-in-One Image Restoration
School of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术学院，哈尔滨工业大学）；School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术学院，哈尔滨工业大学（深圳））
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
School of Computing, Australian National University（澳大利亚国立大学计算学院）；School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳）计算机科学与技术学院）
All-in-One Video Restoration under Smoothly Evolving Unknown Weather Degradations
Department of Computer Science and Technology, Harbin Institute of Technology（计算机科学与技术系，哈尔滨工业大学）；Peng Cheng Laboratory, Shenzhen, China（鹏城实验室，深圳，中国）；School of Computer Science, Peking University（计算机学院，北京大学）
