{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:45:21Z","timestamp":1760237121023,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,26]],"date-time":"2020-02-26T00:00:00Z","timestamp":1582675200000},"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":["61801316"],"award-info":[{"award-number":["61801316"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the National Postdoctoral Program for Innovative Talents of China","award":["BX201700163"],"award-info":[{"award-number":["BX201700163"]}]},{"name":"the Chengdu Industrial Cluster Collaborative Innovation Project","award":["2016-XT00-00015-GX"],"award-info":[{"award-number":["2016-XT00-00015-GX"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Remote sensing images have been widely used in many applications. However, the resolution of the obtained remote sensing images may not meet the increasing demands for some applications. In general, the sparse representation-based super-resolution (SR) method is one of the most popular methods to solve this issue. However, traditional sparse representation SR methods do not fully exploit the complementary constraints of images. Therefore, they cannot accurately reconstruct the unknown HR images. To address this issue, we propose a novel adaptive joint constraint (AJC) based on sparse representation for the single remote sensing image SR. First, we construct a nonlocal constraint by using the nonlocal self-similarity. Second, we propose a local structure filter according to the local gradient of the image and then construct a local constraint. Next, the nonlocal and local constraints are introduced into the sparse representation-based SR framework. Finally, the parameters of the joint constraint model are selected adaptively according to the level of image noise. We utilize the alternate iteration algorithm to tackle the minimization problem in AJC. Experimental results show that the proposed method achieves good SR performance in preserving image details and significantly improves the objective evaluation indices.<\/jats:p>","DOI":"10.3390\/s20051276","type":"journal-article","created":{"date-parts":[[2020,2,27]],"date-time":"2020-02-27T03:21:16Z","timestamp":1582773676000},"page":"1276","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Single Remote Sensing Image Super-Resolution with an Adaptive Joint Constraint Model"],"prefix":"10.3390","volume":"20","author":[{"given":"Lingli","family":"Fu","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5347-2728","authenticated-orcid":false,"given":"Chao","family":"Ren","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"},{"name":"Key Laboratory of Wireless Power Transmission of Ministry of Education, College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohai","family":"He","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaohong","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyong","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu 610065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5148","DOI":"10.1109\/TGRS.2017.2702596","article-title":"Remote sensing scene classification by unsupervised representation learning","volume":"55","author":"Lu","year":"2017","journal-title":"IEEE Trans. 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