{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T12:38:25Z","timestamp":1774960705719,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T00:00:00Z","timestamp":1575590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61871460, 61876152"],"award-info":[{"award-number":["61871460, 61876152"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Basic Research Plan in Shaanxi Province of China","award":["2018JM6066"],"award-info":[{"award-number":["2018JM6066"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["3102019ghxm016"],"award-info":[{"award-number":["3102019ghxm016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet employs the hybrid dilated convolution (HDC) in lightweight network architecture to enlarge the receptive field and aggregate global information. Then, a simple yet effective attention module, convolutional block attention module (CBAM), is integrated into the proposed model to constitute a residual HDC attention block through skip connection, which further enhances representation power and performance of the model. Extensive experimental results on the synthetic and real SAR images demonstrate the superior performance of HDRANet over the state-of-the-art methods in terms of quantitative metrics and visual quality.<\/jats:p>","DOI":"10.3390\/rs11242921","type":"journal-article","created":{"date-parts":[[2019,12,6]],"date-time":"2019-12-06T10:41:44Z","timestamp":1575628904000},"page":"2921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling"],"prefix":"10.3390","volume":"11","author":[{"given":"Jingyu","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science, School of Software, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, School of Software, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Yayuan","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Computer Science, School of Software, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6923-672X","authenticated-orcid":false,"given":"Yunpeng","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Computing and Information Systems, The University of Melbourne, Victoria 3010, Australia"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2277512","article-title":"A tutorial on speckle reduction in synthetic aperture radar images","volume":"1","author":"Argenti","year":"2013","journal-title":"IEEE Geosci. 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