{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,29]],"date-time":"2025-11-29T07:55:28Z","timestamp":1764402928702,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2019,3,23]],"date-time":"2019-03-23T00:00:00Z","timestamp":1553299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["F2016201142\uff0cF2016201187\uff0cF2018210148"],"award-info":[{"award-number":["F2016201142\uff0cF2016201187\uff0cF2018210148"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61401308\uff0c61572063"],"award-info":[{"award-number":["61401308\uff0c61572063"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science research project of Hebei Province","award":["QN2016085"],"award-info":[{"award-number":["QN2016085"]}]},{"name":"Natural Science Foundation of Hebei University","award":["2014-303"],"award-info":[{"award-number":["2014-303"]}]},{"name":"Opening Foundation of Machine vision Engineering Research Center of Hebei Province","award":["2018HBMV02"],"award-info":[{"award-number":["2018HBMV02"]}]},{"name":"Post-graduate's Innovation Fund Project of Hebei Universit","award":["hbu2018ss01"],"award-info":[{"award-number":["hbu2018ss01"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Coherent noise often interferes with synthetic aperture radar (SAR), which has a huge impact on subsequent processing and analysis. This paper puts forward a novel algorithm involving the convolutional neural network (CNN) and guided filtering for SAR image denoising, which combines the advantages of model-based optimization and discriminant learning and considers how to obtain the best image information and improve the resolution of the images. The advantages of proposed method are that, firstly, an SAR image is filtered via five different level denoisers to obtain five denoised images, in which the efficient and effective CNN denoiser prior is employed. Later, a guided filtering-based fusion algorithm is used to integrate the five denoised images into a final denoised image. The experimental results indicate that the algorithm cannot eliminate noise, but it does improve the visual effect of the image significantly, allowing it to outperform some recent denoising methods in this field.<\/jats:p>","DOI":"10.3390\/rs11060702","type":"journal-article","created":{"date-parts":[[2019,3,25]],"date-time":"2019-03-25T06:56:52Z","timestamp":1553497012000},"page":"702","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":70,"title":["Convolutional Neural Network and Guided Filtering for SAR Image Denoising"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7520-8226","authenticated-orcid":false,"given":"Shuaiqi","family":"Liu","sequence":"first","affiliation":[{"name":"College of Electronic and Information Engineering, Hebei University, Baoding 071000, China"}]},{"given":"Tong","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Hebei University, Baoding 071000, China"}]},{"given":"Lele","family":"Gao","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Hebei University, Baoding 071000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8456-7984","authenticated-orcid":false,"given":"Hailiang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China"}]},{"given":"Qi","family":"Hu","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Hebei University, Baoding 071000, China"}]},{"given":"Jie","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic and Information Engineering, Hebei University, Baoding 071000, China"}]},{"given":"Chong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geophysics and Geomatics, China University of Geosciences, Wuhan 430074, China"},{"name":"Bureau of Economic Geology, University of Texas at Austin, Austin, TX 78713-8924, USA"}]}],"member":"1968","published-online":{"date-parts":[[2019,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/S0146-664X(81)80018-4","article-title":"Refined filtering of image noise using local statistics","volume":"15","author":"Lee","year":"1981","journal-title":"Comput. 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