{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T08:29:16Z","timestamp":1765268956178,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T00:00:00Z","timestamp":1704412800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"publisher","award":["2021A1515012009"],"award-info":[{"award-number":["2021A1515012009"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The integration of deep neural networks into sparse synthetic aperture radar (SAR) imaging is explored to enhance SAR imaging performance and reduce the system\u2019s sampling rate. However, the scarcity of training samples and mismatches between the training data and the SAR system pose significant challenges to the method\u2019s further development. In this paper, we propose a novel SAR imaging approach based on deep image prior powered by RED (DeepRED), enabling unsupervised SAR imaging without the need for additional training data. Initially, DeepRED is introduced as the regularization technique within the sparse SAR imaging model. Subsequently, variable splitting and the alternating direction method of multipliers (ADMM) are employed to solve the imaging model, alternately updating the magnitude and phase of the SAR image. Additionally, the SAR echo simulation operator is utilized as an observation model to enhance computational efficiency. Through simulations and real data experiments, we demonstrate that our method maintains imaging quality and system downsampling rate on par with deep-neural-network-based sparse SAR imaging but without the requirement for training data.<\/jats:p>","DOI":"10.3390\/rs16020212","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T03:43:00Z","timestamp":1704426180000},"page":"212","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["DeepRED Based Sparse SAR Imaging"],"prefix":"10.3390","volume":"16","author":[{"given":"Yao","family":"Zhao","sequence":"first","affiliation":[{"name":"Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"Qingsong","family":"Liu","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou 510006, China"}]},{"given":"He","family":"Tian","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Scattering and Radiation, Beijing 100854, China"},{"name":"Beijing Institute of Environment Features, Beijing 100854, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0633-7224","authenticated-orcid":false,"given":"Bingo Wing-Kuen","family":"Ling","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou 510006, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3192-3476","authenticated-orcid":false,"given":"Zhe","family":"Zhang","sequence":"additional","affiliation":[{"name":"Suzhou Key Laboratory of Microwave Imaging, Processing and Application Technology, Suzhou 215000, China"},{"name":"Suzhou Aerospace Information Research Institute, Suzhou 215000, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"National Key Laboratory of Microwave Imaging Technology, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,5]]},"reference":[{"key":"ref_1","unstructured":"Curlander, J.C., and Mcdonough, R.N. 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