{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T19:22:49Z","timestamp":1773775369579,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T00:00:00Z","timestamp":1644796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41901410"],"award-info":[{"award-number":["41901410"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42104036"],"award-info":[{"award-number":["42104036"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41701390"],"award-info":[{"award-number":["41701390"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41906168"],"award-info":[{"award-number":["41906168"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Open Research Fund Program of the Key Laboratory of Digital Mapping and Land Information Application Engineering, NASG, China","award":["ZRZYBWD201905"],"award-info":[{"award-number":["ZRZYBWD201905"]}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["1908085QD161"],"award-info":[{"award-number":["1908085QD161"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hefei Municipal Natural Science Foundation","award":["2021041"],"award-info":[{"award-number":["2021041"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The noise2noise-based despeckling method, capable of training the despeckling deep neural network with only noisy synthetic aperture radar (SAR) image, has presented very good performance in recent research. This method requires a fine-registered multi-temporal dataset with minor time variance and uses similarity estimation to compensate for the time variance. However, constructing such a training dataset is very time-consuming and may not be viable for a certain practitioner. In this article, we propose a novel single-image-capable speckling method that combines the similarity-based block-matching and noise referenced deep learning network. The denoising network designed for this method is an encoder\u2013decoder convolutional neural network and is accommodated to small image patches. This method firstly constructs a large number of noisy pairs as training input by similarity-based block-matching in either one noisy SAR image or multiple images. Then, the method trains the network in a Siamese manner with two parameter-sharing branches. The proposed method demonstrates favorable despeckling performance with both simulated and real SAR data with respect to other state-of-the-art reference filters. It also presents satisfying generalization capability as the trained network can despeckle well the unseen image of the same sensor. The main advantage of the proposed method is its application flexibility. It could be trained with either one noisy image or multiple images. Furthermore, the despeckling could be inferred by either the ad hoc trained network or a pre-trained one of the same sensor.<\/jats:p>","DOI":"10.3390\/rs14040931","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T20:58:03Z","timestamp":1644872283000},"page":"931","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["SAR Image Despeckling Based on Block-Matching and Noise-Referenced Deep Learning Method"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9371-4867","authenticated-orcid":false,"given":"Chen","family":"Wang","sequence":"first","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2216-1203","authenticated-orcid":false,"given":"Zhixiang","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1354-8035","authenticated-orcid":false,"given":"Xiaoshuang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"},{"name":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, Hefei 230601, China"},{"name":"Anhui Province Key Laboratory of Wetland Ecosystem Protection and Restoration, Anhui University, Hefei 230601, China"},{"name":"Engineering Center for Geographic Information of Anhui Province, Hefei 230601, China"}]},{"given":"Zhutao","family":"Yang","sequence":"additional","affiliation":[{"name":"Central Southern China Electric Power Design Institute Co., Ltd., Wuhan 430071, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,14]]},"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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