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{ "extrinsic": { "provider": "https://deposit.softwareheritage.org/1/private/1244/meta/", "raw": { "origin": { "type": "deposit", "url": "https://doi.org/10.5201/ipol.2014.86" }, "origin_metadata": { "metadata": { "atom:external_identifier": "ipol.2014.86", "atom:title": "ipol_86", "codemeta:applicationCategory": "Image Processing", "codemeta:author": { "codemeta:affiliation": "Sadjad University of Mashhad, Mashhad, Iran", "codemeta:name": "Boshra Rajaei" }, "codemeta:dateCreated": "2014-04-22", "codemeta:datePublished": "2020-11-25", "codemeta:description": "An Analysis and Improvement of the BLS-GSM Denoising Method", "codemeta:downloadUrl": "http://www.ipol.im/pub/art/2014/86/blsgm_v1.0.tar.gz", "codemeta:identifier": "https://doi.org/10.5201/ipol.2014.86", "codemeta:isPartOf": { "codemeta:identifier": "ISSN: 2105-1232 DOI: 10.5201/ipol", "codemeta:name": "Image Processing On Line (IPOL)", "codemeta:type": "Journal" }, "codemeta:keywords": [ "Gaussian mixture method", "Bayesian least squares", "denoising method" ], "codemeta:license": { "codemeta:name": "GPL-3.0-or-later", "codemeta:url": "https://spdx.org/licenses/GPL-3.0-or-later.html" }, "codemeta:operatingSystem": "Linux", "codemeta:programmingLanguage": "C", "codemeta:referencePublication": { "codemeta:abstract": "Modeling image properties using Gaussian scale mixture (GSM) model in a multiresolution transform space is the basic idea of a denoising algorithm proposed by Portilla et al. Under this model and using the correlations between pyramid coefficients, the Bayesian least squares (BLS) of each coefficient is used to estimate its original value. In this article, we analyze and discuss the BLS-GSM algorithm, its drawbacks and benefits in more detail. An analytical parameter study of this denoising approach is provided as well. Additionally, we propose a localized version of this algorithm and experimentally show that it outperforms the original method both numerically and visually. We also show that the resulting method is state-of-the-art in terms of PSNR.", "codemeta:identifier": "https://doi.org/10.5201/ipol.2014.86", "codemeta:name": "An Analysis and Improvement of the BLS-GSM Denoising Method", "codemeta:url": "http://www.ipol.im/pub/art/2014/86/article.pdf" }, "codemeta:relatedLink": "http://demo.ipol.im/demo/86/", "codemeta:releaseNotes": "This code implements the algorithm(s) published in the IPOL paper \"An Analysis and Improvement of the BLS-GSM Denoising Method\"", "codemeta:url": "http://www.ipol.im/pub/art/2014/86/", "codemeta:version": "1.0" }, "provider": { "metadata": {}, "provider_name": "", "provider_type": "deposit_client", "provider_url": "https://doi.org/10.5201/" }, "tool": { "configuration": { "sword_version": "2" }, "name": "swh-deposit", "version": "0.8.0" } } }, "when": "2020-12-19T14:42:09.331390+00:00" }, "original_artifact": [ { "checksums": { "sha1": "448b551a81a2afec8617cbf2f1f0258c8ff74807", "sha256": "a3fa90b4aad4ec5315ac8e1f8df507062be2c4cb3368dc4d26c1633a79fed26f" }, "filename": "archive.zip", "length": 1039156, "url": "https://deposit.softwareheritage.org/1/private/1244/raw/" } ] }
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File | Mode | Size |
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blsgsm_v1.0 |
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