All-in-One Image Restoration (AiOIR) has advanced significantly, offering promising solutions for complex real-world degradations. However, most existing approaches rely heavily on degradation-specific representations, often resulting in oversmoothing and artifacts. To address this, we propose ClearAIR, a novel AiOIR framework inspired by Human Visual Perception (HVP) and designed with a hierarchical, coarse-to-fine restoration strategy. First, leveraging the global priority of early HVP, we employ a Multimodal Large Language Model (MLLM)-based Image Quality Assessment (IQA) model for overall evaluation. Unlike conventional IQA, our method integrates cross-modal understanding to more accurately characterize complex, composite degradations. Building upon this overall assessment, we then introduce a region awareness and task recognition pipeline. A semantic cross-attention, leveraging semantic guidance unit, first produces coarse semantic prompts. Guided by this regional context, a degradation-aware module implicitly captures region-specific degradation characteristics, enabling more precise local restoration. Finally, to recover fine details, we propose an internal clue reuse mechanism. It operates in a self-supervised manner to mine and leverage the intrinsic information of the image itself, substantially enhancing detail restoration. Experimental results show that ClearAIR achieves superior performance across diverse synthetic and real-world datasets.
Unified Restoration-Perception Learning: Maritime Infrared-Visible Image Fusion and Segmentation
Laboratory of Underwater Acoustic Communication and Marine Information Technology, Ministry of Education, Xiamen University（厦门大学水声通信与海洋信息技术教育部重点实验室）；PAMI Research Group, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系PAMI研究组）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
Technion – Israel Institute of Technology（以色列理工学院）
Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this paper, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true f…
Unsupervised Variational Translator for Bridging Image Restoration and High-Level Vision Tasks
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区智能系统工程学院）；Guangdong Provincial Key Laboratory of Fire Science and Technology（广东省火灾科学与技术重点实验室）
Perceive-IR: Learning to Perceive Degradation Better for All-in-One Image Restoration
National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University（国家多媒体软件工程研究中心，计算机学院，武汉大学）；School of Computer Science, Wuhan University（计算机学院，武汉大学）；Horizon Robotics（_horizon robotics）；School of Information Engineering, Guangdong University of Technology（信息工程学院，广东技术大学）
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）
Tsinghua University（清华大学）；Chinese University of Hong Kong（香港中文大学）；University of Rochester（罗切斯特大学）
Deep learning methods have witnessed the great progress in image restoration with specific metrics (e.g., PSNR, SSIM). However, the perceptual quality of the restored image is relatively subjective, and it is necessary for users to control the reconstruction result according to personal preferences or image characteristics, which cannot be done using existing deterministic networks. This motivates us to exquisitely design a unified interactive framework for general image restoration tasks. Under this framework, users can control continuous transition of different objectives, e.g., the percepti…
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）
Benchmarking Endoscopic Surgical Image Restoration and Beyond
The Chinese University of Hong Kong（香港中文大学）；Dalian University of Technology（大连理工大学）；Southern Medical University（南方医科大学）；Xidian University（西安电子科技大学）；Wuhan University（武汉大学）
Intra and Inter Parser-Prompted Transformers for Effective Image Restoration
Sun Yat-sen University（中山大学）；Centre for Advances in Reliability and Safety（可靠性与安全先进中心）；The Hong Kong Polytechnic University（香港理工大学）；Nanjing University of Science and Technology（南京理工大学）；Dalian University of Technology（大连理工大学）
Compositional-Degradation UAV Image Restoration: Conditional Decoupled MoE Network and A Benchmark
Key Laboratory of Intelligent Computing & Signal Processing (Anhui University), Ministry of Education（教育部智能计算与信号处理重点实验室（安徽大学））；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）
An Intelligent Agentic System for Complex Image Restoration Problems
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The University of Sydney（悉尼大学）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
本文提出 AgenticIR 智能体系统，结合 LLM、VLM 与 IR 模型工具箱，通过感知、调度、执行、反思和重调度流程及自我探索机制处理复杂图像复原任务。
Binarized High-Efficiency RAW Video Restoration and Beyond
School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Chinese Aeronautical Establishment（中国航空研究院）；Westlake University（西湖大学）；Shanghai Jiao Tong University（上海交通大学）
IR-Flow: Bridging Discriminative and Generative Image Restoration via Rectified Flow
MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, School of Information Science and Technology, University of Science and Technology of China（脑启发智能感知与认知联合实验室，信息科学与技术学院，中国科学技术大学）
School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School（北京大学深圳研究生院电子与计算机工程学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）；Center for Biomedical Image Computing & Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image resto…
Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）
Deep Convolutional Neural Network (CNN) features have been demonstrated to be effective perceptual quality features. The perceptual loss, based on feature maps of pre-trained CNN's has proven to be remarkably effective for CNN based perceptual image restoration problems. In this work, taking inspiration from the the Human Visual System (HVS) and visual perception, we propose a spatial attention mechanism based on the dependency human contrast sensitivity on spatial frequency. We identify regions in input images, based on the underlying spatial frequency, which are not generally well reconstruc…
How far have we gone in Generative Image Restoration? A study on its capability, limitations and evaluation practices
Fudan University（复旦大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of the Chinese Academy of Sciences（中国科学院大学）；Multimedia Laboratory, The Chinese University of Hong Kong（香港中文大学多媒体实验室）；Shenzhen University of Advanced Technology（深圳先进技术大学）
A Task-Driven Evaluation of UAV Detection and Tracking under Synthetic Fog
Center for Aerospace Research (CfAR), University of Victoria（维多利亚大学航空航天研究中心）；Advanced Control and Intelligent Systems (ACIS) Lab, University of Victoria（维多利亚大学先进控制与智能系统实验室）
Harbin Institute of Technology（哈尔滨工业大学）；Peking University（北京大学）
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal p…
Rethinking RGB Color Representation for Image Restoration Models
Seoul National University（首尔大学）；Hanyang University（汉阳大学）；ASRI（ASRI（高级科学技术研究所））；Interdisciplinary Program in Artificial Intelligence, Seoul National University（首尔大学人工智能跨学科项目）
Degradation-Aware Adaptive Context Gating for Unified Image Restoration
School of Computing and Artificial Intelligence, Southwest Jiaotong University（计算机与人工智能学院，西南交通大学）；Institute for Infocomm Research, Agency for Science, Technology and Research（信息通信研究院，科技研究局）；School of Computing and Artificial Intelligence, Jiangxi University of Finance and Economics（计算机与人工智能学院，江西财经大学）；School of Information Management, Jiangxi University of Finance and Economics（信息管理学院，江西财经大学）；Engineering Research Center of Sustainable Urban Intelligent Transportation, Ministry of Education（可持续智慧城市交通工程研究中心，教育部）；National Engineering Laboratory of Integrated Transportation Big Data Application Technology, Southwest Jiaotong University（综合交通运输大数据应用技术国家工程实验室，西南交通大学）
UniV2D: Bridging Visual Restoration and Semantic Perception for Underwater Salient Object Detection
School of Computer Science, Wuhan University（武汉大学计算机学院）；School of Computer Science, The University of Sydney（悉尼大学计算机学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Modern applications such as self-driving cars and drones rely heavily upon robust object detection techniques. However, weather corruptions can hinder the object detectability and pose a serious threat to their navigation and reliability. Thus, there is a need for efficient denoising, deraining, and restoration techniques. Generative adversarial networks and transformers have been widely adopted for image restoration. However, the training of these methods is often unstable and time-consuming. Furthermore, when used for object detection (OD), the output images generated by these methods may pr…
Fourier-RWKV: A Multi-State Perception Network for Efficient Image Dehazing
Institute of Intelligent Information Processing, Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen Key Laboratory of Modern Communications and Information Processing, Shenzhen University（智能信息处理研究院）；College of Engineering and Computer Science, Australian National University（工程与计算机科学学院）
The lower the distortion of an estimator, the more the distribution of its outputs generally deviates from the distribution of the signals it attempts to estimate. This phenomenon, known as the perception-distortion tradeoff, has captured significant attention in image restoration, where it implies that fidelity to ground truth images comes at the expense of perceptual quality (deviation from statistics of natural images). However, despite the increasing popularity of performing comparisons on the perception-distortion plane, there remains an important open question: what is the minimal distor…
JarvisIR: Elevating Autonomous Driving Perception with Intelligent Image Restoration
Xiamen University（厦门大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Bytedance’s Pico（字节跳动Pico）；Tencent（腾讯）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The Chinese University of Hong Kong（香港中文大学）
CAWM-Mamba: A unified model for infrared-visible image fusion and compound adverse weather restoration
School of Physics（物理学院）；Optoelectronic Engineering, Foshan University, Foshan 528225, China（光学电子工程学院，佛山大学，佛山528225，中国）；Guangdong-HongKong-Macao Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology, Foshan 528225, China（粤港澳联合智能微纳光电子技术实验室，佛山528225，中国）；School of Mechanical Electronic（机械电子学院）；Information Engineering, China University of Mining and Technology, Beijing 100083, China（信息工程学院，中国矿业大学，北京100083，中国）
Indian Institute of Technology, Madras（印度理工学院马德拉斯分校）
Convolutional neural network (CNN) based methods have recently achieved great success for image super-resolution (SR). However, most deep CNN based SR models attempt to improve distortion measures (e.g. PSNR, SSIM, IFC, VIF) while resulting in poor quantified perceptual quality (e.g. human opinion score, no-reference quality measures such as NIQE). Few works have attempted to improve the perceptual quality at the cost of performance reduction in distortion measures. A very recent study has revealed that distortion and perceptual quality are at odds with each other and there is always a trade-o…
Breaking Degradation Coupling: A Structural Entropy Guided Decoupled Framework and Benchmark for Infrared Enhancement
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；Department of Biomedical Engineering, Hefei University of Technology（合肥工业大学生物医学工程系）；School of Mathematics and Statistics, Yunnan University（云南大学数学与统计学院）
Position: Evaluation of Visual Processing Should Be Human-Centered, Not Metric-Centered
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；The University of Hong Kong（香港大学）；INSAIT, Sofia University ``St. Kliment Ohridski''（INSAIT，索菲亚大学『圣克莱孟·奥赫里迪斯』）；Shenzhen Loop Area Institute（深圳河套学院）；The Chinese University of Hong Kong（香港中文大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Fudan University（复旦大学）；Shenzhen University of Advanced Technology（深圳先进技术大学）；Shanghai AI Laboratory（上海人工智能实验室）
Beijing Jiaotong University（北京交通大学）；Beijing Key Laboratory of Traffic Data Analysis and Mining（北京市交通数据分析与挖掘重点实验室）；CAAC Key Laboratory of Intelligent Passenger Service of Civil Aviation（中国民航局民航智能旅客服务重点实验室）
The Light Field (LF) deblurring task is a challenging problem as the blur images are caused by different reasons like the camera shake and the object motion. The single image deblurring method is a possible way to solve this problem. However, since it deals with each view independently and cannot effectively utilize and maintain the LF structure, the restoration effect is usually not ideal. Besides, the LF blur is more complex because the degree is affected by the views and depth. Therefore, we carefully designed a novel LF deblurring network based on the LF blur characteristics. On one hand, …
