{"id":13543477,"url":"https://github.com/csjunxu/TWSC-ECCV2018","last_synced_at":"2025-04-02T13:30:28.867Z","repository":{"id":54635631,"uuid":"139690097","full_name":"csjunxu/TWSC-ECCV2018","owner":"csjunxu","description":" Matlab Code for \"A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising, ECCV 2018\".","archived":false,"fork":false,"pushed_at":"2020-03-24T09:09:54.000Z","size":10686,"stargazers_count":91,"open_issues_count":6,"forks_count":26,"subscribers_count":10,"default_branch":"master","last_synced_at":"2024-11-03T10:32:56.004Z","etag":null,"topics":["denoising-images","image-processing"],"latest_commit_sha":null,"homepage":"","language":"MATLAB","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/csjunxu.png","metadata":{"files":{"readme":"README.md","changelog":"ChangeLog.txt","contributing":null,"funding":null,"license":"License.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2018-07-04T08:09:10.000Z","updated_at":"2024-06-11T14:33:46.000Z","dependencies_parsed_at":"2022-08-13T22:20:17.314Z","dependency_job_id":null,"html_url":"https://github.com/csjunxu/TWSC-ECCV2018","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csjunxu%2FTWSC-ECCV2018","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csjunxu%2FTWSC-ECCV2018/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csjunxu%2FTWSC-ECCV2018/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/csjunxu%2FTWSC-ECCV2018/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/csjunxu","download_url":"https://codeload.github.com/csjunxu/TWSC-ECCV2018/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246823513,"owners_count":20839736,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["denoising-images","image-processing"],"created_at":"2024-08-01T11:00:32.150Z","updated_at":"2025-04-02T13:30:28.111Z","avatar_url":"https://github.com/csjunxu.png","language":"MATLAB","funding_links":[],"categories":["Denoising Algorithms"],"sub_categories":[],"readme":"The code in this package implements the Trilateral Weighted Sparse Coding Scheme for real color image denoising as described in the following paper:\n\n```\n@article{TWSC_ECCV2018,        \n         author = {Jun Xu and Lei Zhang and David Zhang},        \n         title = {A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising},        \n         journal = {ECCV},       \n         year = {2018}     \n}\n```\n\nPlease cite the paper if you feel this code useful in your research.\nPlease see the file License.txt for the license governing this code.\n\n  Version:       1.0 (13/07/2018), see ChangeLog.txt\n  Contact:       Jun Xu \u003ccsjunxu@comp.polyu.edu.hk, nankaimathxujun@gmail.com\u003e\n\n\nTest\n------------\n1. Run \"Demo_TWSC_Sigma_AWGN.m\" for Additive White Gaussian noise removal.\n2. Run \"Demo_TWSC_Sigma_RW*.m\" for Real-world noise removal.\nNote: Please set \"Original_image_dir\" according to your case.\n\nData\n------------\nPlease download the data from corresponding addresses.\n1. cleanimages: 20 high quality commonly used natural gray scale images\n\n2. nc: real noisy images with no ''ground truth''\n                        This dataset can be found at http://demo.ipol.im/demo/125/\n3. cc: 15 cropped real noisy images from CC [1]. \n                        This dataset can be found at  http://snam.ml/research/ccnoise\n                        The smaller 15 cropped images can be found on in the directory \n                        ''Real_ccnoise_denoised_part'' of \n                        https://github.com/csjunxu/MCWNNM_ICCV2017\n                                                The *real.png are noisy images;\n                                                The *mean.png are \"ground truth\" images;\n                                                The *ours.png are images denoised by CC.\n4. dnd: The Darmstadt Noise Dataset [2] consists of 50 pairs of real noisy images, \n             each images provides 50 crops, resulting overall 1,000 crops provided on\n             https://noise.visinf.tu-darmstadt.de/\n\n[1] A Holistic Approach to Cross-Channel Image Noise Modeling and its Application to Image Denoising. \n     Seonghyeon Nam*, Youngbae Hwang*, Yasuyuki Matsushita, Seon Joo Kim. CVPR 2016.\n\n[2] Benchmarking Denoising Algorithms with Real Photographs. Tobias Plötz and Stefan Roth. CVPR 2017.\n\nDependency\n------------\nThis code is implemented purely in Matlab2014b and doesn't depends on any other toolbox.\n\nContact\n------------\nIf you have any questions or suggestions with the code, or find a bug, please let us know. \nContact Jun Xu at csjunxu@comp.polyu.edu.hk or nankaimathxujun@gmail.com.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsjunxu%2FTWSC-ECCV2018","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcsjunxu%2FTWSC-ECCV2018","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcsjunxu%2FTWSC-ECCV2018/lists"}