This paper addresses the limitations of adverse weather image restoration approaches trained on synthetic data when applied to real-world scenarios. We formulate a semi-supervised learning framework employing vision-language models to enhance restoration performance across diverse adverse weather conditions in real-world settings. Our approach involves assessing image clearness and providing semantics using vision-language models on real data, serving as supervision signals for training restoration models. For clearness enhancement, we use real-world data, utilizing a dual-step strategy with pseudo-labels assessed by vision-language models and weather prompt learning. For semantic enhancement, we integrate real-world data by adjusting weather conditions in vision-language model descriptions while preserving semantic meaning. Additionally, we introduce an effective training strategy to bootstrap restoration performance. Our approach achieves superior results in real-world adverse weather image restoration, demonstrated through qualitative and quantitative comparisons with state-of-the-art works.
Prompt to Restore, Restore to Prompt: Cyclic Prompting for Universal Adverse Weather Removal
School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；School of Information and Communication Engineering, University of Electronic Science and Technology of China（电子科技大学信息与通信工程学院）；School of Computer Science and Technology, Beijing Jiaotong University（北京交通大学计算机科学与技术学院）
Degradation-Aware Prompt Learning with Cross-Modal Compensation for Adverse Weather Removal
School of Software Engineering, Dalian University（大连大学软件工程学院）；School of Mathematical Sciences, Dalian University of Technology（大连理工大学数学科学学院）；The Hong Kong Polytechnic University（香港理工大学）；University of California, San Francisco（加利福尼亚大学旧金山分校）；School of Computer Science and Engineering, Nanjing University of Science and Technology（南京理工大学计算机科学与工程学院）
Tsinghua University（清华大学）；Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science（中国科学院深圳先进技术研究院）；Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；University College London（伦敦大学学院）
Most state-of-the-art semantic segmentation approaches only achieve high accuracy in good conditions. In practically-common but less-discussed adverse environmental conditions, their performance can decrease enormously. Existing studies usually cast the handling of segmentation in adverse conditions as a separate post-processing step after signal restoration, making the segmentation performance largely depend on the quality of restoration. In this paper, we propose a novel deep-learning framework to tackle semantic segmentation and image restoration in adverse environmental conditions in a hol…
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Harbin Institute of Technology, Shenzhen（哈尔滨工业大学（深圳））；Hong Kong University of Science and Technology（香港科技大学）；Rakuten Institute of Technology（乐天理工研究院）；Imperial College London（伦敦帝国学院）；KAIST（韩国科学技术院）；Tencent（腾讯）；Australian National University（澳大利亚国立大学）；Nanjing University（南京大学）
Uncertainty-Guided Adverse Weather Restoration via Gated Transformer Network
School of Automation, Southeast University（东南大学自动化学院）；Chair of Robotics, Artificial Intelligence and Real-time Systems, Technical University of Munich（慕尼黑工业大学机器人、人工智能与实时系统讲席教授组）
Clear Roads, Clear Vision: Advancements in Multi-Weather Restoration for Smart Transportation
Finolex Academy of Management and Technology（Finolex管理与技术学院）；Drone Lab, Centre for Artificial Intelligence and Robotics, IIT Mandi（印度理工学院曼迪分校人工智能与机器人中心无人机实验室）；Mehta Family School of Data Science and Artificial Intelligence, IIT Guwahati（印度理工学院古瓦哈提分校梅塔家族数据科学与人工智能学院）；SPACE42；Centre of Excellence: Artificial Intelligence, School of Computer Science, UPES Dehradun（UPES德拉敦分校计算机科学学院人工智能卓越中心）
Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China（香港中文大学（深圳）计算机科学与工程系）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）；ROAS Thrust, the Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China and The Hong Kong University of Science and Technology, Department of Electronic and Computer Engineering, Hong Kong SAR, China（香港科学与技术大学（广州）ROAS方向及电子与计算机工程系，香港特别行政区）
Wavelet-Enhanced Desnowing: A Novel Single Image Restoration Approach for Traffic Surveillance under Adverse Weather Conditions
Automatic Control Research Institute, China Nuclear Power Engineering Co., Ltd.（中国核电工程有限公司自动控制研究院）；CNNC Engineering Research Center for Fuel Reprocessing（中核集团燃料后处理工程研究中心）；Chinese Aeronautical Establishment, Aviation Industry Corporation of China, Ltd（中国航空工业集团有限公司中国航空研究院）
In recent years, convolutional neural network-based single image adverse weather removal methods have achieved significant performance improvements on many benchmark datasets. However, these methods require large amounts of clean-weather degraded image pairs for training, which is often difficult to obtain in practice. Although various weather degradation synthesis methods exist in the literature, the use of synthetically generated weather degraded images often results in sub-optimal performance on the real weather degraded images due to the domain gap between synthetic and real-world images. …
Traffic Image Restoration under Adverse Weather via Frequency-Aware Mamba
School of Communication and Information Engineering, Shanghai University（上海大学通信与信息工程学院）；College of Electronic Science, Air Force Aviation University（空军航空大学电子科学学院）；Intelligent Visual Information Perception Laboratory, Institute of Advanced Technology, Nanjing University of Posts and Telecommunications（南京邮电大学先进技术研究院智能视觉信息感知实验室）；School of Computer Science and Technology, East China Normal University（华东师范大学计算机科学与技术学院）
Removing adverse weather conditions like rain, fog, and snow from images is an important problem in many applications. Most methods proposed in the literature have been designed to deal with just removing one type of degradation. Recently, a CNN-based method using neural architecture search (All-in-One) was proposed to remove all the weather conditions at once. However, it has a large number of parameters as it uses multiple encoders to cater to each weather removal task and still has scope for improvement in its performance. In this work, we focus on developing an efficient solution for the a…
Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Lingnan University（岭南大学）；Hong Kong Metropolitan University（香港都会大学）
Adverse weather conditions such as haze, rain, and snow often impair the quality of captured images, causing detection networks trained on normal images to generalize poorly in these scenarios. In this paper, we raise an intriguing question - if the combination of image restoration and object detection, can boost the performance of cutting-edge detectors in adverse weather conditions. To answer it, we propose an effective yet unified detection paradigm that bridges these two subtasks together via dynamic enhancement learning to discern objects in adverse weather conditions, called TogetherNet.…
Nankai International Advanced Research Institute（南开大学国际高等研究院）；Peng Cheng Laboratory（鹏城实验室）；College of Computer Science, Nankai University（南开大学计算机学院）；University of Science and Technology Beijing（北京科技大学）；University of Macau（澳门大学）
Diffusion-Based Restoration for Multi-Modal 3D Object Detection in Adverse Weather
School of Xingzhi College, South China Normal University（星智学院，华南师范大学）；College of Big Data and Internet, Shenzhen Technology University（大数据与互联网学院，深圳科技大学）；School of Data Science and Engineering, Xingzhi College, South China Normal University（数据科学与工程学院，星智学院，华南师范大学）；Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology（电子与计算机工程系，香港科技大学）；College of Intelligence Science and Technology, National University of Defense Technology（智能科学与技术学院，国防科技大学）
When Color-Space Decoupling Meets Diffusion for Adverse-Weather Image Restoration
School of Computer Science and Engineering, Nanjing University of Science and Technology（计算机科学与工程学院，南京理工大学）；School of Electrical Engineering, Southeast University（电气工程学院，东南大学）；School of Intelligence Science and Technology, Nanjing University（智能科学与技术学院，南京大学）
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）
Small compression noises, despite being transparent to human eyes, can adversely affect the results of many image restoration processes, if left unaccounted for. Especially, compression noises are highly detrimental to inverse operators of high-boosting (sharpening) nature, such as deblurring and superresolution against a convolution kernel. By incorporating the non-linear DCT quantization mechanism into the formulation for image restoration, we propose a new sparsity-based convex programming approach for joint compression noise removal and image restoration. Experimental results demonstrate s…
Visual recognition under adverse conditions is a very important and challenging problem of high practical value, due to the ubiquitous existence of quality distortions during image acquisition, transmission, or storage. While deep neural networks have been extensively exploited in the techniques of low-quality image restoration and high-quality image recognition tasks respectively, few studies have been done on the important problem of recognition from very low-quality images. This paper proposes a deep learning based framework for improving the performance of image and video recognition model…
WeatherRemover: All-in-one Adverse Weather Removal with Multi-scale Feature Map Compression
Guangdong University of Technology（广东工业大学）；Sanda University（上海杉达学院）；University of Queensland（昆士兰大学）；Shenzhen MSU-BIT University（深圳北理莫斯科大学）；Zhejiang college of Security Technology（浙江安防职业技术学院）；Northwest China Research Institute of Electronic Equipment（西北电子设备研究所）；Shenzhen Research Institute of Big Data（深圳市大数据研究院）；Shenzhen University（深圳大学）；Misr Higher Institute for Commerce and Computers（米斯尔商业与计算机高等学院）
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
School of Telecommunications Engineering, Xidian University（西安电子科技大学通信工程学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；Hefei Comprehensive National Science Center（合肥综合性国家科学中心）；University of New South Wales (UNSW)（新南威尔士大学）；Shandong Normal University（山东师范大学）
ResLPR: A LiDAR Data Restoration Network and Benchmark for Robust Place Recognition Against Weather Corruptions
College of Intelligence Science and Technology（智能科学与技术学院）；National University of Defense Technology（国防科技大学）；Harbin Institute of Technology（哈尔滨工业大学）；New York University Abu Dhabi（纽约大学阿布扎比分校）；University of Science and Technology of China（中国科学技术大学）
UniDet-D: A Unified Dynamic Spectral Attention Model for Object Detection under Adverse Weathers
School of Interdisciplinary Science, Beijing Institute of Technology（交叉学科学院，北京理工大学）；North China University of Technology（华北理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing, Beijing Institute of Technology（空间智能信息处理国家重点实验室，北京理工大学）；School of Computer Science and Engineering, Nanyang Technological University（计算机科学与工程学院，南洋理工大学）
Restore, Assess, Repeat: A Unified Framework for Iterative Image Restoration
Samsung AI Center Cambridge（三星剑桥人工智能中心）；National Taiwan University（国立台湾大学）；Technical University of Iasi（雅西科技大学）；Chang Gung University（长庚大学）；University of Wurzburg（维尔茨堡大学）；Queen Mary University of London（伦敦玛丽女王大学）
Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines
School of Electrical Engineering and Automation, Nantong University（南通大学电气工程及其自动化学院）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）；College of Computer and Software Engineering, Hohai University（河海大学计算机与软件学院）；School of Electronic and Information Engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
Delving into Cascaded Instability: A Lipschitz Continuity View on Image Restoration and Object Detection Synergy
Sun Yat-sen University（中山大学）；Australian National University（澳大利亚国立大学）；Harbin Institute of Technology（哈尔滨工业大学）；Tsinghua University（清华大学）；Peng Cheng Laboratory（鹏城实验室）
Enhancing Image Restoration Transformer via Adaptive Translation Equivariance
Institute of Medical Technology, Peking University Health Science Center, Peking University（北京大学医学部医学技术研究所）；Biomedical Engineering Department, College of Future Technology, Peking University（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）
Jimei University（集美大学）；Southwest University（西南大学）；China University of Petroleum（中国石油大学）
Removing adverse weather conditions like rain, fog, and snow from images is a challenging problem. Although the current recovery algorithms targeting a specific condition have made impressive progress, it is not flexible enough to deal with various degradation types. We propose an efficient and compact image restoration network named DAN-Net (Degradation-Adaptive Neural Network) to address this problem, which consists of multiple compact expert networks with one adaptive gated neural. A single expert network efficiently addresses specific degradation in nasty winter scenes relying on the compa…
