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                    "codemeta:author": {
                        "codemeta:affiliation": "Yale University, USA",
                        "codemeta:name": "Pascal Getreuer"
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                    "codemeta:dateCreated": "2012-05-16",
                    "codemeta:datePublished": "2012-05-19",
                    "codemeta:description": "Rudin-Osher-Fatemi Total Variation Denoising using Split Bregman",
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                        "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol",
                        "codemeta:name": "Image Processing On Line (IPOL)",
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                    "codemeta:keywords": [
                        "image denoising",
                        "total variation"
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                        "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"
                    },
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                    "codemeta:releaseNotes": "This package implements the Rudin-Osher-Fatemi Total Variation Denoising Algorithm using Split Bregman.",
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