{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T01:52:29Z","timestamp":1775181149204,"version":"3.50.1"},"reference-count":86,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T00:00:00Z","timestamp":1673395200000},"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":["61901309"],"award-info":[{"award-number":["61901309"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Remotely sensed images degraded by additive white Gaussian noise (AWGN) have low-level vision, resulting in a poor analysis of their contents. To reduce AWGN, two types of denoising strategies, sparse-coding-model-based and deep-neural-network-based (DNN), are commonly utilized, which have their respective merits and drawbacks. For example, the former pursue enjoyable performance with a high computational burden, while the latter have powerful capacity in completing a specified task efficiently, but this limits their application range. To combine their merits for improving performance efficiently, this paper proposes a model-driven deep denoising (MD3) scheme. To solve the MD3 model, we first decomposed it into several subproblems by the alternating direction method of multipliers (ADMM). Then, the denoising subproblems are replaced by different learnable denoisers, which are plugged into the unfolded MD3 model to efficiently produce a stable solution. Both quantitative and qualitative results validate that the proposed MD3 approach is effective and efficient, while it has a more powerful ability in generating enjoyable denoising performance and preserving rich textures than other advanced methods.<\/jats:p>","DOI":"10.3390\/rs15020445","type":"journal-article","created":{"date-parts":[[2023,1,12]],"date-time":"2023-01-12T03:11:02Z","timestamp":1673493062000},"page":"445","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["MD3: Model-Driven Deep Remotely Sensed Image Denoising"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3128-2405","authenticated-orcid":false,"given":"Zhenghua","family":"Huang","sequence":"first","affiliation":[{"name":"Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China"},{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8834-9060","authenticated-orcid":false,"given":"Zifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaozong","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7113-731X","authenticated-orcid":false,"given":"Zhicheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Biyun","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Information Engineering, Wuhan Institute of Technology, Wuhan 430205, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8943-079X","authenticated-orcid":false,"given":"Jun","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Astronautics, Northwestern Polytechnical University (NWPU), Xi\u2019an 710072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Wuhan Donghu University, Wuhan 430212, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1109\/TCYB.2020.2989241","article-title":"Error-tolerant deep learning for remote sensing image scene classification","volume":"51","author":"Li","year":"2021","journal-title":"IEEE Trans. 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