乐胖代购免代理版

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Group sparse representation (GSR) is powerful for image restoration; it however often leads to undesirable sparse solutions in practice. In order to improve the quality of image restoration based on GSR, the sparsity residual model expects the representation learned from degraded images to be as close as possible to the true representation. In this article, a group residual learning based on low\u2010rank self\u2010representation is proposed to automatically estimate the true group sparse representation. It makes full use of the relation among patches and explores the subgroup structures within the same group, which makes the sparse residual model have better interpretation furthermore, results in high\u2010quality restored images. Extensive experimental results on two typical image restoration tasks (image denoising and deblocking) demonstrate that the proposed algorithm outperforms many other popular or state\u2010of\u2010the\u2010art image restoration methods.<\/jats:p>","DOI":"10.1049\/ipr2.12982","type":"journal-article","created":{"date-parts":[[2023,11,11]],"date-time":"2023-11-11T04:35:10Z","timestamp":1699677310000},"page":"741-760","update-policy":"http:\/\/dx.doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Image restoration with group sparse representation and low\u2010rank group residual learning"],"prefix":"10.1049","volume":"18","author":[{"given":"Zhaoyuan","family":"Cai","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology Xidian University Xi'an Shaanxi China"}]},{"given":"Xianghua","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Computer Science Swansea University Swansea UK"}]},{"given":"Jingjing","family":"Deng","sequence":"additional","affiliation":[{"name":"Department of Computer Science Durham University Durham UK"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5162-6244","authenticated-orcid":false,"given":"Zengfa","family":"Dou","sequence":"additional","affiliation":[{"name":"20th Research Institute China Electronic Science and Technology Group Co., Ltd Xi'an Shaanxi China"}]},{"given":"Bo","family":"Tong","sequence":"additional","affiliation":[{"name":"Xi'an Thermal Power Research Institute Co., Ltd Xi'an China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-5604-7137","authenticated-orcid":false,"given":"Xiaoke","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology Xidian University Xi'an Shaanxi China"}]}],"member":"265","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/79.581363"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2006.881969"},{"key":"e_1_2_10_4_1","doi-asserted-by":"crossref","unstructured":"Yang J. 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