Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
Nanjing University（南京大学）；Memorial University of Newfoundland（纽芬兰纪念大学）；Australian National University（澳大利亚国立大学）；Sun Yat-sen University（中山大学）；Wenzhou University（温州大学）
Tao Wang, Guangpin Tao, Wanglong Lu, Kaihao Zhang, Wenhan Luo, Xiaoqin Zhang, Tong Lu
Image restoration under hazy weather condition, which is called single image dehazing, has been of significant interest for various computer vision applications. In recent years, deep learning-based methods have achieved success. However, existing image dehazing methods typically neglect the hierarchy of features in the neural network and fail to exploit their relationships fully. To this end, we propose an effective image dehazing method named Hierarchical Contrastive Dehazing (HCD), which is based on feature fusion and contrastive learning strategies. HCD consists of a hierarchical dehazing network (HDN) and a novel hierarchical contrastive loss (HCL). Specifically, the core design in the HDN is a hierarchical interaction module, which utilizes multi-scale activation to revise the feature responses hierarchically. To cooperate with the training of HDN, we propose HCL which performs contrastive learning on hierarchically paired exemplars, facilitating haze removal. Extensive experiments on public datasets, RESIDE, HazeRD, and DENSE-HAZE, demonstrate that HCD quantitatively outperforms the state-of-the-art methods in terms of PSNR, SSIM and achieves better visual quality.
Restoring Images in Adverse Weather Conditions via Histogram Transformer
Institute of Information Engineering, Chinese Academy of Sciences（中国科学院信息工程研究所）；School of Cyber Security, University of Chinese Academy of Sciences（中国科学院大学网络安全学院）；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）；Guangdong Provincial Key Laboratory of Information Security Technology（广东省信息安全技术重点实验室）；Wechat Business Group, Tencent（腾讯微信事业群）
Infrared-Assisted Single-Stage Framework for Joint Restoration and Fusion of Visible and Infrared Images under Hazy Conditions
Faculty of Information Engineering and Automation（信息工程与自动化学院）；Key Laboratory of Artificial Intelligence in Yunnan Province（云南省人工智能重点实验室）；School of Instrument Science and Opto-Electronic Engineering（仪器科学与光电工程学院）
School of Engineering Science, University of Science and Technology of China（中国科学技术大学工程科学学院）；College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics and Astronautics（南京航空航天大学机电学院）；School of Data Science, University of Science and Technology of China（中国科学技术大学数据科学学院）
Adverse weather conditions such as haze and rain corrupt the quality of captured images, which cause detection networks trained on clean images to perform poorly on these images. To address this issue, we propose an unsupervised prior-based domain adversarial object detection framework for adapting the detectors to hazy and rainy conditions. In particular, we use weather-specific prior knowledge obtained using the principles of image formation to define a novel prior-adversarial loss. The prior-adversarial loss used to train the adaptation process aims to reduce the weather-specific informatio…
School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）；Rolls-Royce Plc（劳斯莱斯公司）；Rolls-Royce Derby（劳斯莱斯德比）
This paper presents a multi-sensor architecture with an adaptive multi-sensor management system suitable for control and navigation of autonomous maritime vessels in hazy and poor-visibility conditions. This architecture resides in the autonomous maritime vessels. It augments the data from on-board imaging sensors and weather sensors with the AIS data and weather data from sensors on other vessels and the on-shore vessel traffic surveillance system. The combined data is analyzed using computational intelligence and data analytics to determine suitable course of action while utilizing historica…
Kunming University of Science and Technology（昆明理工大学）；China University of Petroleum (East China)（中国石油大学（华东））
In a conventional domain adaptation person Re-identification (Re-ID) task, both the training and test images in target domain are collected under the sunny weather. However, in reality, the pedestrians to be retrieved may be obtained under severe weather conditions such as hazy, dusty and snowing, etc. This paper proposes a novel Interference Suppression Model (ISM) to deal with the interference caused by the hazy weather in domain adaptation person Re-ID. A teacherstudent model is used in the ISM to distill the interference information at the feature level by reducing the discrepancy between …
University of Karachi（卡拉奇大学）；Kyung Hee University（庆熙大学）
Images captured in hazy weather conditions often suffer from color contrast and color fidelity. This degradation is represented by transmission map which represents the amount of attenuation and airlight which represents the color of additive noise. In this paper, we have proposed a method to estimate the transmission map using haze levels instead of airlight color since there are some ambiguities in estimation of airlight. Qualitative and quantitative results of proposed method show competitiveness of the method given. In addition we have proposed two metrics which are based on statistics of …
Multi-Branch Non-Homogeneous Image Dehazing via Concentration Partitioning and Image Fusion
School of Software and Internet of Things Engineering, Zhejiang Gongshang University（软件与物联网工程学院，浙江工商大学）；School of Computer Science and Technology, Tiangong University（计算机科学与技术学院，天工大学）
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…
Building Extraction from Remote Sensing Imagery under Hazy and Low-light Conditions: Benchmark and Baseline
MOE Key Lab of ICSP（教育部ICSP重点实验室）；Anhui Provincial Key Lab of Multimodal Cognitive Computation（安徽省多模态认知计算重点实验室）；IMIS Lab of Anhui Province（安徽省IMIS实验室）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；Graduate School of Frontier Sciences, The University of Tokyo（东京大学前沿科学研究生院）
MvKSR: Multi-view Knowledge-guided Scene Recovery for Hazy and Rainy Degradation
The Hong Kong Polytechnic University（香港理工大学）；Wuhan University of Technology（武汉理工大学）；Wuhan Baosight Software Co., Ltd（武汉宝信软件有限公司）；Singapore Management University（新加坡管理大学）
Chongqing University（重庆大学）；Chongqing Normal University（重庆师范大学）；Changan Auto Company（长安汽车公司）；CRRC Zhuzhou Institute Co.,Ltd（中车株洲电力机车研究所有限公司）
The adverse haze weather condition has brought considerable difficulties in vision-based environmental applications. While, until now, most of the existing environmental monitoring studies are under ordinary conditions, and the studies of complex haze weather conditions have been ignored. Thence, this paper proposes a feature-supervised learning network based on generative adversarial networks (GAN) for environmental monitoring during hazy days. Its main idea is to train the model under the supervision of feature maps from the ground truth. Four key technical contributions are made in the pape…
ReviveDiff: A Universal Diffusion Model for Restoring Images in Adverse Weather Conditions
Faculty of Engineering and Information Technology, University of Technology Sydney（工程与信息技术学院，技术悉尼大学）；PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, School of Computer Science and Engineering, Nanjing University of Science and Technology（智能感知与高维信息系统教育部重点实验室，计算机科学与工程学院，南京理工大学）；Key Laboratory of Artificial Intelligence, Ministry of Education（人工智能教育部重点实验室）
Video Adverse-Weather-Component Suppression Network via Weather Messenger and Adversarial Backpropagation
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；University of Cambridge（剑桥大学）；Institute of High Performance Computing, Agency for Science, Technology and Research（新加坡科技研究局高性能计算研究所）；Tianjin University（天津大学）；Hong Kong Metropolitan University（香港都会大学）
DA2Diff: Exploring Degradation-aware Adaptive Diffusion Priors for All-in-One Weather Restoration
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）；Collaborative Innovation Center of Novel Software Technology and Industrialization（新型软件技术与产业化协同创新中心）；College 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）；Tsinghua Shenzhen International Graduate School, Tsinghua University（清华大学深圳国际研究生院）
Existing approaches for restoring weather-degraded images follow a fully-supervised paradigm and they require paired data for training. However, collecting paired data for weather degradations is extremely challenging, and existing methods end up training on synthetic data. To overcome this issue, we describe an approach for supervising deep networks that are based on CycleGAN, thereby enabling the use of unlabeled real-world data for training. Specifically, we introduce new losses for training CycleGAN that lead to more effective training, resulting in high-quality reconstructions. These new …
