When smartphone cameras are used to take photos of digital screens, usually moire patterns result, severely degrading photo quality. In this paper, we design a wavelet-based dual-branch network (WDNet) with a spatial attention mechanism for image demoireing. Existing image restoration methods working in the RGB domain have difficulty in distinguishing moire patterns from true scene texture. Unlike these methods, our network removes moire patterns in the wavelet domain to separate the frequencies of moire patterns from the image content. The network combines dense convolution modules and dilated convolution modules supporting large receptive fields. Extensive experiments demonstrate the effectiveness of our method, and we further show that WDNet generalizes to removing moire artifacts on non-screen images. Although designed for image demoireing, WDNet has been applied to two other low-levelvision tasks, outperforming state-of-the-art image deraining and derain-drop methods on the Rain100h and Raindrop800 data sets, respectively.
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）
Wavelet-based Decoupling Framework for low-light Stereo Image Enhancement
Shaanxi Key Laboratory for Network Computing and Security Technology（陕西网络计算与安全技术重点实验室）；School of Computer Science and Engineering（计算机科学与工程学院）；Xi'an University of Technology（西安理工大学）
GDSR: Global-Detail Integration through Dual-Branch Network with Wavelet Losses for Remote Sensing Image Super-Resolution
School of Computer Technology and Application, Qinghai University（青海大学计算机技术与应用学院）；Qinghai Provincial Laboratory for Intelligent Computing and Application, Qinghai University（青海省智能计算与应用实验室）；Department of Computer Science and Technology, Tsinghua University（清华大学计算机科学与技术系）；School of Computer and Information Science, Qinghai Institute of Technology（青海理工大学计算机与信息科学学院）；College of Agriculture and Animal Husbandry, Qinghai University（青海大学农业与动物科学学院）
Fractal image compression, Culik's image compression and zerotree prediction coding of wavelet image decomposition coefficients succeed only because typical images being compressed possess a significant degree of self-similarity. Besides the common concept, these methods turn out to be even more tightly related, to the point of algorithmical reducibility of one technique to another. The goal of the present paper is to demonstrate these relations. The paper offers a plain-term interpretation of Culik's image compression, in regular image processing terms, without resorting to finite state machi…
Spatial-Frequency-Aware Implicit Neural Representation of Multidimensional Signals via MLP-KAN Fusion
Institute of Computational Imaging, Beijing Information Science and Technology University（北京信息科技大学计算成像研究所）；College of Computer Science (College of Software), Inner Mongolia University（内蒙古大学计算机学院（软件学院））；Research Center for Spatiotemporal Intelligence, Inner Mongolia University（内蒙古大学时空智能研究中心）
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）
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
College of Computer Science (College of Software), Inner Mongolia University, Hohhot, 010021, China（内蒙古大学计算机学院（软件学院））；School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China（武汉大学数学与统计学院）；Institute of Computational Imaging, Beijing Information Science and Technology University, Beijing 102206, China（北京信息科技大学计算成像研究院）
Wavelet-Guided Dual-Frequency Encoding for Remote Sensing Change Detection
School of Computer Science and Technology, Shandong Technology and Business University（山东科技职业大学计算机科学与技术学院）；College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；College of Computer Science and Technology, Qingdao University（青岛大学计算机科学与技术学院）；School of Computer Science and Engineering, South China University of Technology（华南理工大学计算机科学与工程学院）
ARFC-WAHNet: Adaptive Receptive Field Convolution and Wavelet-Attentive Hierarchical Network for Infrared Small Target Detection
School of Information and Communication Engineering（信息与通信工程学院）；Laboratory of Imaging Detection and Intelligent Perception（成像检测与智能感知实验室）；University of Electronic Science and Technology of China（电子科学与技术大学）
HFS-TriNet: A Three-Branch Collaborative Feature Learning Network for Prostate Cancer Classification from TRUS Videos
Guangdong Polytechnic Normal University（广东 polytechnic 正规大学）；Guangdong Provincial Key Laboratory of Intellectual Property & Big Data（广东省知识产权与大数据重点实验室）；The First Affiliated Hospital of Jinan University（暨南大学第一附属医院）；Shenzhen People’s Hospital（深圳人民医院）
Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection
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）；Shenzhen Key Laboratory of Visual Object Detection and Recognition（深圳市视觉目标检测与识别重点实验室）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
Layer-wise Noise Guided Selective Wavelet Reconstruction for Robust Medical Image Segmentation
Big Data and Software College, Chongqing University（重庆大学大数据与软件学院）；Office of Scientific Research, Peking University（北京大学科研办公室）；Key Lab of High Confidence Software Technology, Ministry of Education (Peking University)（教育部高可信软件技术重点实验室（北京大学））；School of Computer Science, Peking University（北京大学计算机学院）；Department of Cardiology and Institute of Vascular Medicine, Peking University Third Hospital（北京大学第三医院心内科和血管医学研究院）
WaveInst: A Frequency-Domain Enhanced Network for Fine-Grained Thin Tree Trunk Extraction in Forest Scenes
College of Information Science and Technology & Artificial Intelligence, Nanjing Forestry University（南京林业大学信息科学与人工智能学院）；Department of Geography, College of Natural Resources and Environment, Virginia Tech（维吉尼亚理工大学地理系）；Institute of Forest Resource Information Techniques, Chinese Academy of Forestry（中国林业科学研究院森林资源信息技术研究所）；State Forestry and Grassland Administration, Key Laboratory of Forest Management and Growth Modelling（国家林业和草原局森林管理与生长建模重点实验室）；School of Geospatial Artificial Intelligence, East China Normal University（华东师范大学地理空间人工智能学院）
