Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net.
National University of Singapore（新加坡国立大学）；Nanyang Technological University（南洋理工大学）；Sun Yat-Sen University（中山大学）；Swinburne University of Technology（斯威本科技大学）
The quality of images captured in outdoor environments can be affected by poor weather conditions such as fog, dust, and atmospheric scattering of other particles. This problem can bring extra challenges to high-level computer vision tasks like image segmentation and object detection. However, previous studies on image dehazing suffer from a huge computational workload and corruption of the original image, such as over-saturation and halos. In this paper, we present a novel image dehazing approach based on the optical model for haze images and regularized optimization. Specifically, we convert…
WaveDH: Wavelet Sub-bands Guided ConvNet for Efficient Image Dehazing
Artificial Intelligence Graduate School, Gwangju Institute of Science and Technology (GIST)（光州科学技术院人工智能研究生院）；School of Electronic Engineering, Xidian University（西安电子科技大学电子工程学院）；School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST)（光州科学技术院电气与计算机工程学院）
WDMamba: When Wavelet Degradation Prior Meets Vision Mamba for Image Dehazing
School of Computer Science and Technology, Anhui University of Technology（安徽理工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）；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）；College of Artificial Intelligence, Taiyuan University of Technology（太原科技大学人工智能学院）
We propose a fusion algorithm for haze removal that combines color information from an RGB image and edge information extracted from its corresponding NIR image using Haar wavelets. The proposed algorithm is based on the key observation that NIR edge features are more prominent in the hazy regions of the image than the RGB edge features in those same regions. To combine the color and edge information, we introduce a haze-weight map which proportionately distributes the color and edge information during the fusion process. Because NIR images are, intrinsically, nearly haze-free, our work makes …
University of Science and Technology of China（中国科学技术大学）；Hong Kong University of Science and Technology（香港科技大学）；Shandong University（山东大学）；City University of Hong Kong（香港城市大学）；University of Central Florida（中佛罗里达大学）；Microsoft Cloud and AI（微软云与人工智能部门）
Image dehazing aims to recover the uncorrupted content from a hazy image. Instead of leveraging traditional low-level or handcrafted image priors as the restoration constraints, e.g., dark channels and increased contrast, we propose an end-to-end gated context aggregation network to directly restore the final haze-free image. In this network, we adopt the latest smoothed dilation technique to help remove the gridding artifacts caused by the widely-used dilated convolution with negligible extra parameters, and leverage a gated sub-network to fuse the features from different levels. Extensive ex…
Haze-Aware Attention Network for Single-Image Dehazing
School of Ocean Information Engineering, Jimei University（集美大学海洋信息工程学院）；College of Artificial Intelligence, Southwest University（西南大学人工智能学院）；School of Computer Science, Jimei University（集美大学计算机科学学院）
Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Technical University of Munich（慕尼黑工业大学）；Shanghai Jiao Tong University（上海交通大学）；Tsinghua University（清华大学）
Efficient Dual-domain Image Dehazing with Haze Prior Perception
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）
UR2P-Dehaze: Learning a Simple Image Dehaze Enhancer via Unpaired Rich Physical Prior
College of Computer Science and Engineering（计算机科学与工程学院）；Chongqing University of Technology（重庆理工大学）；School of Science, Engineering and Environment（科学、工程与环境学院）；University of Salford（索尔福德大学）；College of Computer Science（计算机学院）；Chongqing University（重庆大学）
Multi-dimensional Visual Prompt Enhanced Image Restoration via Mamba-Transformer Aggregation
School of Digital Industry, Jiangxi Normal University（江西师范大学数字产业学院）；School of Computer and Information Engineering, Jiangxi Normal University（江西师范大学计算机与信息工程学院）
We develop a new physical model for the rain effect and show that the well-known atmosphere scattering model (ASM) for the haze effect naturally emerges as its homogeneous continuous limit. Via depth-aware fusion of multi-layer rain streaks according to the camera imaging mechanism, the new model can better capture the sophisticated non-deterministic degradation patterns commonly seen in real rainy images. We also propose a Densely Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining and dehazing tasks. Our design alleviates the bottleneck issue existent in conventiona…
Fi-Gaussian: Frequency-Aware Implicit Gaussian Splatting for Single Image Dehazing
College of Mechanical and Vehicle Engineering, Chongqing University（重庆大学机械与车辆工程学院）；Department of Electrical and Computer Engineering, Carnegie Mellon University（卡内基梅隆大学电气与计算机工程系）；National Innovation Center of Intelligent and Connected Vehicles（国家智能网联汽车创新中心）；School of Vehicle and Mobility, Tsinghua University（清华大学车辆与运载学院）
WaveDM: Wavelet-Based Diffusion Models for Image Restoration
Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；University of Chinese Academy of Sciences（中国科学院大学）；The University of Hong Kong（香港大学）
National Yang Ming Chiao Tung University（阳明交通大学）；University at Albany - SUNY（奥尔巴尼大学 - 纽约州立大学）；University at Buffalo（布法罗大学）
Pooling is a simple but essential layer in modern deep CNN architectures for feature aggregation and extraction. Typical CNN design focuses on the conv layers and activation functions, while leaving the pooling layers with fewer options. We introduce the Learning Discrete Wavelet Pooling (LDW-Pooling) that can be applied universally to replace standard pooling operations to better extract features with improved accuracy and efficiency. Motivated from the wavelet theory, we adopt the low-pass (L) and high-pass (H) filters horizontally and vertically for pooling on a 2D feature map. Feature sign…
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
organization= Research Center for Translational Medicine, Medical Innovation Center；State Key Laboratory of Cardiology, Shanghai East Hospital , city= Shanghai , postcode= 200120 , country= China；organization= The Institute for Biomedical Engineering \& Nano Science, Tongji University School of Medicine , city= Shanghai , postcode= 200120 , country= China；organization= Shanghai Research Institute for Intelligent Autonomous Systems , city= Shanghai , postcode= 201210 , country= China；organization= Department of Ophthalmology, Tongji Hospital, School of Medicine, Tongji University , city= Shanghai , postcode= 200000 , country= China；organization= Ophthalmology, Isra Postgraduate Institute of Ophthalmology , city= Karachi , postcode= , country= Pakistan；organization= The Eye Centre, South City Hospital , city= Karachi , postcode= , country= Pakistan；organization= Center for the Development of Laboratory Equipment, Pakistan Council of Scientific；Industrial Research , city= Karachi , postcode= , country= Pakistan
Image inpainting is the task of filling masked or unknown regions of an image with visually realistic contents, which has been remarkably improved by Deep Neural Networks (DNNs) recently. Essentially, as an inverse problem, the inpainting has the underlying challenges of reconstructing semantically coherent results without texture artifacts. Many previous efforts have been made via exploiting attention mechanisms and prior knowledge, such as edges and semantic segmentation. However, these works are still limited in practice by an avalanche of learnable prior parameters and prohibitive computat…
GeoCueFormer: Geometry-Guided Wavelet Representation and Prediction-Cued Dual-Stage Decoder for Underwater Semantic Segmentation
Southwest University of Science and Technology（西南科技大学）；Columbia University（哥伦比亚大学）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Berkeley（加州大学伯克利分校）
PRISM: Progressive Rain removal with Integrated State-space Modeling
Faculty of Data Science, City University of Macau, SAR Macao, China（澳门城市大学数据科学学院）；College of Oceanography and Space Informatics, China University of Petroleum (East China), China（中国石油大学（华东）海洋学院）；Zhuhai 4Dage Network Technology, China（珠海4Dage网络技术）
RDNet: Region Proportion-Aware Dynamic Adaptive Salient Object Detection Network in Optical Remote Sensing Images
School of Control Science and Engineering, Shandong University（控制科学与工程学院，山东大学）；Key Laboratory of Machine Intelligence and System Control, Ministry of Education（教育部智能信息与系统控制重点实验室）；School of Automation, Hangzhou Dianzi University（自动化学院，杭州电子科技大学）；School of Mathematics and Computer Science, Lishui University（数学与计算机科学学院，丽水大学）；School of Data Science, Lingnan University（数据科学学院，岭南大学）
