{"id":29984947,"url":"https://github.com/theveryhim/classic-denoising","last_synced_at":"2025-08-04T21:17:19.551Z","repository":{"id":306845752,"uuid":"1027379292","full_name":"theveryhim/Classic-Denoising","owner":"theveryhim","description":"Hands on project deploying typical denoising methods.","archived":false,"fork":false,"pushed_at":"2025-07-27T23:59:41.000Z","size":15179,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-07-28T01:22:17.399Z","etag":null,"topics":["image-denoising","image-processing"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/theveryhim.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-07-27T22:44:15.000Z","updated_at":"2025-07-28T00:07:15.000Z","dependencies_parsed_at":"2025-07-28T01:35:38.235Z","dependency_job_id":null,"html_url":"https://github.com/theveryhim/Classic-Denoising","commit_stats":null,"previous_names":["theveryhim/classic-denoising"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/theveryhim/Classic-Denoising","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FClassic-Denoising","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FClassic-Denoising/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FClassic-Denoising/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FClassic-Denoising/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/theveryhim","download_url":"https://codeload.github.com/theveryhim/Classic-Denoising/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theveryhim%2FClassic-Denoising/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":268777604,"owners_count":24306013,"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","status":"online","status_checked_at":"2025-08-04T02:00:09.867Z","response_time":79,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["image-denoising","image-processing"],"created_at":"2025-08-04T21:17:15.271Z","updated_at":"2025-08-04T21:17:19.500Z","avatar_url":"https://github.com/theveryhim.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Classic Denoising\n\nIn this repo we investigate classic denoising methods. \nOur focus is on `Non-Local Means (NLM), BM3D, Total Variation (TV), Weighted Nuclear Norm Minimization (WNNM)`\u003cbr\u003e\nFind more detailed analysis in notebook! \n\n## Adding Noise\nadditive Gaussian noise with an average of zero and a deviation from a standard equal to 0.02 of the maximum brightness available in a set of 130 images\n\n## Denoising\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/1.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n```\nWNNM PSNR: 16.21 dB\nNLM PSNR: 37.32 dB\nBM3D PSNR: 33.40 dB\nTV PSNR: 33.47 dB\n```\n## Second order TV\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/2.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n```\nAverage second_order_tv_psnr: 33.52 dB\n```\n## Denoising clean image\nhere we use the *Prob#1.png* image without adding noise as the input of the denoising methods:\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"images/3.png\" alt=\"Descriptive Alt Text\" class=\"fit-width-image\"\u003e\n\u003c/p\u003e\n\n```\nWNNM PSNR: 30.66 dB\nNLM PSNR: 8.06 dB\nBM3D PSNR: 111.51 dB\nTV PSNR: 8.06 dB\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fclassic-denoising","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheveryhim%2Fclassic-denoising","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheveryhim%2Fclassic-denoising/lists"}