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{ "extrinsic": { "provider": "https://deposit.softwareheritage.org/1/private/1192/meta/", "raw": { "origin": { "type": "deposit", "url": "https://doi.org/10.5201/ipol.2012.g-tvd" }, "origin_metadata": { "metadata": { "codemeta:applicationCategory": "Image Processing", "codemeta:author": { "codemeta:affiliation": "Yale University, USA", "codemeta:name": "Pascal Getreuer" }, "codemeta:dateCreated": "2012-05-16", "codemeta:datePublished": "2012-05-19", "codemeta:description": "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman", "codemeta:downloadUrl": "https://www.ipol.im/pub/art/2012/g-tvd/tvdenoise_20120516.tar.gz", "codemeta:identifier": "https://doi.org/10.5201/ipol.2012.g-tvd", "codemeta:isPartOf": { "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol", "codemeta:name": "Image Processing On Line (IPOL)", "codemeta:type": "Journal" }, "codemeta:keywords": [ "image denoising", "total variation" ], "codemeta:license": { "codemeta:name": "BSD-2-Clause", "codemeta:url": "https://spdx.org/licenses/BSD-2-Clause.html" }, "codemeta:operatingSystem": "Linux, MacOS, Windows", "codemeta:programmingLanguage": "C", "codemeta:referencePublication": { "codemeta:abstract": "Denoising is the problem of removing noise from an image. The most commonly studied case is with additive white Gaussian noise (AWGN), where the observed noisy image f is related to the underlying true image u by f=u+\u03b7 and \u03b7 is at each point in space independently and identically distributed as a zero-mean Gaussian random variable.\nTotal variation (TV) regularization is a technique that was originally developed for AWGN image denoising by Rudin, Osher, and Fatemi. The TV regularization technique has since been applied to a multitude of other imaging problems, see for example Chan and Shen's book. We focus here on the split Bregman algorithm of Goldstein and Osher for TV-regularized denoising.", "codemeta:identifier": "https://doi.org/10.5201/ipol.2012.g-tvd", "codemeta:name": "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman", "codemeta:url": "https://www.ipol.im/pub/art/2012/g-tvd/article.pdf" }, "codemeta:relatedLink": "http://demo.ipol.im/demo/g_tv_denoising/", "codemeta:releaseNotes": "This package implements the Rudin-Osher-Fatemi Total Variation Denoising Algorithm using Split Bregman.", "codemeta:url": "https://www.ipol.im/pub/art/2012/g-tvd/", "codemeta:version": "20120516", "external_identifier": "ipol.2012.g-tvd", "title": "alde" }, "provider": { "metadata": {}, "provider_name": "", "provider_type": "deposit_client", "provider_url": "https://doi.org/10.5201/" }, "tool": { "configuration": { "sword_version": "2" }, "name": "swh-deposit", "version": "0.6.0" } } }, "when": "2020-12-12T16:47:13.944791+00:00" }, "original_artifact": [ { "checksums": { "sha1": "3aaaf4af6814c05b38ed35f71ab8f79a8065f230", "sha256": "bef4a967d7b2501eb4510ee5be6c3fc7d37ad2b8c24e01993c722191450cf760" }, "filename": "archive.zip", "length": 81529, "url": "https://deposit.softwareheritage.org/1/private/1192/raw/" } ] }
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