{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T08:13:46Z","timestamp":1777623226767,"version":"3.51.4"},"reference-count":31,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,6]],"date-time":"2023-01-06T00:00:00Z","timestamp":1672963200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Technology China (MOST) Major Program on New Generation of Artificial Intelligence 2030","award":["2018AAA0102200"],"award-info":[{"award-number":["2018AAA0102200"]}]},{"name":"Ministry of Science and Technology China (MOST) Major Program on New Generation of Artificial Intelligence 2030","award":["61827814"],"award-info":[{"award-number":["61827814"]}]},{"name":"Ministry of Science and Technology China (MOST) Major Program on New Generation of Artificial Intelligence 2030","award":["JCYJ20190808153619413"],"award-info":[{"award-number":["JCYJ20190808153619413"]}]},{"name":"Natural Science Foundation China (NSFC) Major Project","award":["2018AAA0102200"],"award-info":[{"award-number":["2018AAA0102200"]}]},{"name":"Natural Science Foundation China (NSFC) Major Project","award":["61827814"],"award-info":[{"award-number":["61827814"]}]},{"name":"Natural Science Foundation China (NSFC) Major Project","award":["JCYJ20190808153619413"],"award-info":[{"award-number":["JCYJ20190808153619413"]}]},{"name":"Shenzhen Science and Technology Innovation Commission (SZSTI) Project","award":["2018AAA0102200"],"award-info":[{"award-number":["2018AAA0102200"]}]},{"name":"Shenzhen Science and Technology Innovation Commission (SZSTI) Project","award":["61827814"],"award-info":[{"award-number":["61827814"]}]},{"name":"Shenzhen Science and Technology Innovation Commission (SZSTI) Project","award":["JCYJ20190808153619413"],"award-info":[{"award-number":["JCYJ20190808153619413"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. In practice, however, it can be very challenging to obtain labels in support of this approach. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. Several unsupervised denoisers have emerged in recent years; however, to ensure their effectiveness, the noise model must be determined in advance, which limits the practical use of unsupervised denoising.n addition, obtaining inaccurate noise prior to noise estimation algorithms leads to low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model; the difference is that the model is generated by a residual image and a random mask during the network training process, and the input and target of the network are generated from a single noisy image and the noise model. At the same time, an unsupervised module and a pseudo supervised module are trained. The extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.<\/jats:p>","DOI":"10.3390\/rs15020364","type":"journal-article","created":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T04:47:08Z","timestamp":1673239628000},"page":"364","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Generative Recorrupted-to-Recorrupted: An Unsupervised Image Denoising Network for Arbitrary Noise Distribution"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2994-5393","authenticated-orcid":false,"given":"Yukun","family":"Liu","sequence":"first","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bowen","family":"Wan","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daming","family":"Shi","sequence":"additional","affiliation":[{"name":"College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0371-9646","authenticated-orcid":false,"given":"Xiaochun","family":"Cheng","sequence":"additional","affiliation":[{"name":"Computer Science Department, Middlesex University, Hendon, London NW4 4BT, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3142","DOI":"10.1109\/TIP.2017.2662206","article-title":"Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising","volume":"26","author":"Zhang","year":"2017","journal-title":"IEEE Trans. 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