{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:19:29Z","timestamp":1776122369015,"version":"3.50.1"},"reference-count":153,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,10,28]],"date-time":"2022-10-28T00:00:00Z","timestamp":1666915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"Natural Science Foundation of Shandong Province","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"National Natural Science Foundation of China","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"National Natural Science Foundation of China","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"National Natural Science Foundation of China","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"National Natural Science Foundation of China","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"National Natural Science Foundation of China","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"National Natural Science Foundation of China","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"National Natural Science Foundation of China","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"National Natural Science Foundation of China","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"National Natural Science Foundation of China","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"National Natural Science Foundation of China","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"National Natural Science Foundation of China","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"National Natural Science Foundation of China","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"National Natural Science Foundation of China","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62272405"],"award-info":[{"award-number":["62272405"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62072391"],"award-info":[{"award-number":["62072391"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62066013"],"award-info":[{"award-number":["62066013"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62172351"],"award-info":[{"award-number":["62172351"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62102338"],"award-info":[{"award-number":["62102338"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62273290"],"award-info":[{"award-number":["62273290"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["62103350"],"award-info":[{"award-number":["62103350"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"Shaanxi Key R &amp; D Program","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"Youth Innovation Science and Technology Support Program of Shandong Province","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"Yantai Science and Technology Innovation Development Plan Project","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2020QF108"],"award-info":[{"award-number":["ZR2020QF108"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2022QF037"],"award-info":[{"award-number":["ZR2022QF037"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2020MF148"],"award-info":[{"award-number":["ZR2020MF148"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2020QF031"],"award-info":[{"award-number":["ZR2020QF031"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2020QF046"],"award-info":[{"award-number":["ZR2020QF046"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["ZR2022MF238"],"award-info":[{"award-number":["ZR2022MF238"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62272405"],"award-info":[{"award-number":["62272405"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62072391"],"award-info":[{"award-number":["62072391"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62066013"],"award-info":[{"award-number":["62066013"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62172351"],"award-info":[{"award-number":["62172351"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62102338"],"award-info":[{"award-number":["62102338"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62273290"],"award-info":[{"award-number":["62273290"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["62103350"],"award-info":[{"award-number":["62103350"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["2021M693078"],"award-info":[{"award-number":["2021M693078"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["2021GY-290"],"award-info":[{"award-number":["2021GY-290"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["2021KJ080"],"award-info":[{"award-number":["2021KJ080"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["2021YT06000645"],"award-info":[{"award-number":["2021YT06000645"]}]},{"name":"Open Foundation of State key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications)","award":["SKLNST-2022-1-12"],"award-info":[{"award-number":["SKLNST-2022-1-12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>At present, with the advance of satellite image processing technology, remote sensing images are becoming more widely used in real scenes. However, due to the limitations of current remote sensing imaging technology and the influence of the external environment, the resolution of remote sensing images often struggles to meet application requirements. In order to obtain high-resolution remote sensing images, image super-resolution methods are gradually being applied to the recovery and reconstruction of remote sensing images. The use of image super-resolution methods can overcome the current limitations of remote sensing image acquisition systems and acquisition environments, solving the problems of poor-quality remote sensing images, blurred regions of interest, and the requirement for high-efficiency image reconstruction, a research topic that is of significant relevance to image processing. In recent years, there has been tremendous progress made in image super-resolution methods, driven by the continuous development of deep learning algorithms. In this paper, we provide a comprehensive overview and analysis of deep-learning-based image super-resolution methods. Specifically, we first introduce the research background and details of image super-resolution techniques. Second, we present some important works on remote sensing image super-resolution, such as training and testing datasets, image quality and model performance evaluation methods, model design principles, related applications, etc. Finally, we point out some existing problems and future directions in the field of remote sensing image super-resolution.<\/jats:p>","DOI":"10.3390\/rs14215423","type":"journal-article","created":{"date-parts":[[2022,10,30]],"date-time":"2022-10-30T09:01:42Z","timestamp":1667120502000},"page":"5423","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":121,"title":["A Review of Image Super-Resolution Approaches Based on Deep Learning and Applications in Remote Sensing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7606-1411","authenticated-orcid":false,"given":"Xuan","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Jinglei","family":"Yi","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Jian","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Yongchao","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Jun","family":"Lyu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6688-5014","authenticated-orcid":false,"given":"Jindong","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Weiqing","family":"Yan","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4782-6796","authenticated-orcid":false,"given":"Jindong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}]},{"given":"Qing","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, China"},{"name":"School of Information Science and Technology, University of Science and Technology of China, Hefei 230026, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3609-3574","authenticated-orcid":false,"given":"Haigen","family":"Min","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an 710064, China"},{"name":"The Joint Laboratory for Internet of Vehicles, Ministry of Education-China Mobile Communications Corporation, Xi\u2019an 710064, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jo, Y., and Kim, S.J. (2021, January 20\u201325). Practical single-image super-resolution using look-up table. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00075"},{"key":"ref_2","first-page":"67","article-title":"Image Zooming Using Barycentric Rational Interpolation","volume":"12","author":"Loghmani","year":"2018","journal-title":"J. Math. Ext."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Cherifi, T., Hamami-Metiche, L., and Kerrouchi, S. (2020, January 16\u201317). Comparative study between super-resolution based on polynomial interpolations and Whittaker filtering interpolations. Proceedings of the 2020 1st International Conference on Communications, Control Systems and Signal Processing (CCSSP), El-Oued, Algeria.","DOI":"10.1109\/CCSSP49278.2020.9151673"},{"key":"ref_4","first-page":"9944385","article-title":"Single-Image Super-Resolution Using Panchromatic Gradient Prior and Variational Model","volume":"2021","author":"Xu","year":"2021","journal-title":"Math. Probl. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5904","DOI":"10.1109\/TIP.2018.2860685","article-title":"Single image super-resolution via multiple mixture prior models","volume":"27","author":"Huang","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.isatra.2017.03.001","article-title":"Single image super-resolution using self-optimizing mask via fractional-order gradient interpolation and reconstruction","volume":"82","author":"Yang","year":"2018","journal-title":"ISA Trans."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Xiong, M., Song, Y., Xiang, Y., Xie, B., and Deng, Z. (2021, January 28\u201330). Anchor neighborhood embedding based single-image super-resolution reconstruction with similarity threshold adjustment. Proceedings of the 2021 2nd International Conference on Artificial Intelligence and Information Systems, Chongqing, China.","DOI":"10.1145\/3469213.3470306"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"28713","DOI":"10.1007\/s11042-021-11062-0","article-title":"Single image super-resolution via multiple linear mapping anchored neighborhood regression","volume":"80","author":"Hardiansyah","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108184","DOI":"10.1016\/j.sigpro.2021.108184","article-title":"Single image super-resolution using feature adaptive learning and global structure sparsity","volume":"188","author":"Liu","year":"2021","journal-title":"Signal Process."},{"key":"ref_10","unstructured":"Yang, B., and Wu, G. (2021). Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.patrec.2018.12.008","article-title":"Example-based image super-resolution via blur kernel estimation and variational reconstruction","volume":"117","author":"Yang","year":"2019","journal-title":"Pattern Recognit. Lett."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101973","DOI":"10.1016\/j.compmedimag.2021.101973","article-title":"3D dense convolutional neural network for fast and accurate single MR image super-resolution","volume":"93","author":"Wang","year":"2021","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yutani, T., Yono, O., Kuwatani, T., Matsuoka, D., Kaneko, J., Hidaka, M., Kasaya, T., Kido, Y., Ishikawa, Y., and Ueki, T. (2022). Super-Resolution and Feature Extraction for Ocean Bathymetric Maps Using Sparse Coding. Sensors, 22.","DOI":"10.3390\/s22093198"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2375","DOI":"10.1109\/TIP.2022.3154614","article-title":"TDPN: Texture and Detail-Preserving Network for Single Image Super-Resolution","volume":"31","author":"Cai","year":"2022","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1109\/TASSP.1981.1163711","article-title":"Cubic convolution interpolation for digital image processing","volume":"29","author":"Keys","year":"1981","journal-title":"IEEE Trans. Acoust. Speech, Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1109\/TIP.2009.2012908","article-title":"SoftCuts: A Soft Edge Smoothness Prior for Color Image Super-Resolution","volume":"18","author":"Dai","year":"2009","journal-title":"IEEE Trans. Image Process."},{"key":"ref_17","unstructured":"Chang, H., Yeung, D.Y., and Xiong, Y. (July, January 27). Super-resolution through neighbor embedding. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1109\/TPAMI.2015.2439281","article-title":"Image super-resolution using deep convolutional networks","volume":"38","author":"Dong","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (July, January 26). Deeply-recursive convolutional network for image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"883","DOI":"10.5194\/isprs-archives-XLI-B3-883-2016","article-title":"Single-image super resolution for multispectral remote sensing data using convolutional neural networks","volume":"41","author":"Liebel","year":"2016","journal-title":"ISPRS-Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_21","unstructured":"Kim, J., Lee, J.K., and Lee, K.M. (July, January 26). Accurate image super-resolution using very deep convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1109\/LGRS.2017.2704122","article-title":"Super-resolution for remote sensing images via local\u2013global combined network","volume":"14","author":"Lei","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Guo, Y., Chen, J., Wang, J., Chen, Q., Cao, J., Deng, Z., Xu, Y., and Tan, M. (2020, January 13\u201319). Closed-loop matters: Dual regression networks for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00545"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","article-title":"Deep learning for image super-resolution: A survey","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","unstructured":"Tian, C., Zhang, X., Lin, J.C.W., Zuo, W., and Zhang, Y. (2022). Generative Adversarial Networks for Image Super-Resolution: A Survey. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.inffus.2021.09.005","article-title":"Real-world single image super-resolution: A brief review","volume":"79","author":"Chen","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Liu, H., Ruan, Z., Zhao, P., Dong, C., Shang, F., Liu, Y., Yang, L., and Timofte, R. (2022). Video super-resolution based on deep learning: A comprehensive survey. Artif. Intell. Rev., 1\u201355.","DOI":"10.1007\/s10462-022-10147-y"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2957","DOI":"10.1109\/TMM.2019.2914883","article-title":"Deep Objective Quality Assessment Driven Single Image Super-Resolution","volume":"21","author":"Yan","year":"2019","journal-title":"IEEE Trans. Multimed."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","article-title":"State-of-the-art in artificial neural network applications: A survey","volume":"4","author":"Abiodun","year":"2018","journal-title":"Heliyon"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","article-title":"Representation Learning: A Review and New Perspectives","volume":"35","author":"Bengio","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_32","unstructured":"Martin, D., Fowlkes, C., Tal, D., and Malik, J. (2001, January 9\u201312). A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV 2001), Vancouver, BC, Canada."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"898","DOI":"10.1109\/TPAMI.2010.161","article-title":"Contour detection and hierarchical image segmentation","volume":"33","author":"Arbelaez","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Agustsson, E., and Timofte, R. (2017, January 21\u201316). Ntire 2017 challenge on single image super-resolution: Dataset and study. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.150"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Bevilacqua, M., Roumy, A., Guillemot, C., and Alberi-Morel, M.L. (2012, January 7\u201310). Low-complexity single-image super-resolution based on nonnegative neighbor embedding. Proceedings of the 23rd British Machine Vision Conference (BMVC), Surrey, UK.","DOI":"10.5244\/C.26.135"},{"key":"ref_36","unstructured":"Zeyde, R., Elad, M., and Protter, M. (2010, January 24\u201330). On single image scale-up using sparse-representations. Proceedings of the International Conference on Curves and Surfaces, Avignon, France."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Huang, J.B., Singh, A., and Ahuja, N. (2015, January 7\u201312). Single image super-resolution from transformed self-exemplars. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299156"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.1109\/TGRS.2017.2685945","article-title":"AID: A benchmark data set for performance evaluation of aerial scene classification","volume":"55","author":"Xia","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"2321","DOI":"10.1109\/LGRS.2015.2475299","article-title":"Deep learning based feature selection for remote sensing scene classification","volume":"12","author":"Zou","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1109\/LGRS.2010.2055033","article-title":"Satellite image classification via two-layer sparse coding with biased image representation","volume":"8","author":"Dai","year":"2010","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, X., Yu, K., Dong, C., and Loy, C.C. (2018, January 18\u201323). Recovering realistic texture in image super-resolution by deep spatial feature transform. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00070"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Yang, Y., and Newsam, S. (2010, January 3\u20135). Bag-of-visual-words and spatial extensions for land-use classification. Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, CA, USA.","DOI":"10.1145\/1869790.1869829"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1865","DOI":"10.1109\/JPROC.2017.2675998","article-title":"Remote Sensing Image Scene Classification: Benchmark and State of the Art","volume":"105","author":"Cheng","year":"2017","journal-title":"Proc. IEEE"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"035004","DOI":"10.1117\/1.JRS.10.035004","article-title":"Feature significance-based multibag-of-visual-words model for remote sensing image scene classification","volume":"10","author":"Zhao","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2861","DOI":"10.1109\/TIP.2010.2050625","article-title":"Image super-resolution via sparse representation","volume":"19","author":"Yang","year":"2010","journal-title":"IEEE Trans. Image Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Fujimoto, A., Ogawa, T., Yamamoto, K., Matsui, Y., Yamasaki, T., and Aizawa, K. (2016, January 4). Manga109 dataset and creation of metadata. Proceedings of the 1st International Workshop on Comics Analysis, Processing and Understanding, Cancun, Mexico.","DOI":"10.1145\/3011549.3011551"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Blau, Y., Mechrez, R., Timofte, R., Michaeli, T., and Zelnik-Manor, L. (2018, January 8\u201314). The 2018 PIRM challenge on perceptual image super-resolution. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, Munich, Germany.","DOI":"10.1007\/978-3-030-11021-5_21"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, C., Xiong, Z., Tian, X., Zha, Z.J., and Wu, F. (2019, January 16\u201317). Camera lens super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00175"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., and Fei-Fei, L. (2009, January 20\u201325). Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, USA.","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1007\/s11263-014-0733-5","article-title":"The pascal visual object classes challenge: A retrospective","volume":"111","author":"Everingham","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., and Tang, X. (2015, January 7\u201313). Deep learning face attributes in the wild. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"600","DOI":"10.1109\/TIP.2003.819861","article-title":"Image quality assessment: From error visibility to structural similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","unstructured":"Zhang, K., Zhao, T., Chen, W., Niu, Y., and Hu, J.F. (2022). SPQE: Structure-and-Perception-Based Quality Evaluation for Image Super-Resolution. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/LSP.2012.2227726","article-title":"Making a \u201cCompletely Blind\u201d Image Quality Analyzer","volume":"20","author":"Mittal","year":"2013","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., Efros, A.A., Shechtman, E., and Wang, O. (2018, January 18\u201323). The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00068"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Tai, Y., Yang, J., and Liu, X. (2017, January 21\u201326). Image super-resolution via deep recursive residual network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.298"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Ahn, N., Kang, B., and Sohn, K.A. (2018, January 8\u201314). Fast, accurate, and lightweight super-resolution with cascading residual network. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_16"},{"key":"ref_59","unstructured":"Qiu, Y., Wang, R., Tao, D., and Cheng, J. (November, January 27). Embedded block residual network: A recursive restoration model for single-image super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, J., Yuan, Y., Mei, K., and Fang, F. (November, January 27). Lightweight and Accurate Recursive Fractal Network for Image Super-Resolution. Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea.","DOI":"10.1109\/ICCVW.2019.00474"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Luo, Z., Huang, Y., Li, S., Wang, L., and Tan, T. (2021, January 10\u201315). Efficient Super Resolution by Recursive Aggregation. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412271"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Gao, G., Wang, Z., Li, J., Li, W., Yu, Y., and Zeng, T. (2022). Lightweight Bimodal Network for Single-Image Super-Resolution via Symmetric CNN and Recursive Transformer. arXiv.","DOI":"10.24963\/ijcai.2022\/128"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Tian, Y., Kong, Y., Zhong, B., and Fu, Y. (2018, January 18\u201323). Residual Dense Network for Image Super-Resolution. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00262"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Li, J., Fang, F., Mei, K., and Zhang, G. (2018, January 8\u201314). Multi-scale Residual Network for Image Super-Resolution. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01237-3_32"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Liu, J., Zhang, W., Tang, Y., Tang, J., and Wu, G. (2020, January 13\u201319). Residual feature aggregation network for image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00243"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity mappings in deep residual networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Lim, B., Son, S., Kim, H., Nah, S., and Mu Lee, K. (2017, January 21\u201326). Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.151"},{"key":"ref_69","unstructured":"Qin, J., He, Z., Yan, B., Jeon, G., and Yang, X. (2021, January 14\u201317). Multi-Residual Feature Fusion Network for lightweight Single Image Super-Resolution. Proceedings of the 2021 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC), Tokyo, Japan."},{"key":"ref_70","unstructured":"Park, K., Soh, J.W., and Cho, N.I. (2021). A Dynamic Residual Self-Attention Network for Lightweight Single Image Super-Resolution. IEEE Trans. Multimed."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1271","DOI":"10.1109\/JAS.2021.1004009","article-title":"Lightweight Image Super-Resolution via Weighted Multi-Scale Residual Network","volume":"8","author":"Sun","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"7711","DOI":"10.1109\/TGRS.2021.3049875","article-title":"A Spectral Grouping and Attention-Driven Residual Dense Network for Hyperspectral Image Super-Resolution","volume":"59","author":"Liu","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Liu, J., Tang, J., and Wu, G. (2020, January 23\u201328). Residual Feature Distillation Network for Lightweight Image Super-Resolution. Proceedings of the European conference on computer vision (ECCV), Glasgow, UK.","DOI":"10.1007\/978-3-030-67070-2_2"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lai, W.S., Huang, J.B., Ahuja, N., and Yang, M.H. (2017, January 21\u201326). Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.618"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"6393","DOI":"10.1109\/ACCESS.2022.3142510","article-title":"Multiscale Recursive Feedback Network for Image Super-Resolution","volume":"10","author":"Chen","year":"2022","journal-title":"IEEE Access"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.neucom.2019.10.076","article-title":"Multi-scale feature fusion residual network for Single Image Super-Resolution","volume":"379","author":"Qin","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"166359","DOI":"10.1016\/j.ijleo.2021.166359","article-title":"Single image super-resolution using multi-scale feature enhancement attention residual network","volume":"231","author":"Pandey","year":"2021","journal-title":"Optik"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zeng, H., Guo, S., and Zhang, L. (2022). Efficient Long-Range Attention Network for Image Super-resolution. arXiv.","DOI":"10.1007\/978-3-031-19790-1_39"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., and Fu, Y. (2018, January 8\u201314). Image super-resolution using very deep residual channel attention networks. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Niu, B., Wen, W., Ren, W., Zhang, X., Yang, L., Wang, S., Zhang, K., Cao, X., and Shen, H. (2020, January 23\u201328). Single image super-resolution via a holistic attention network. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_12"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Dai, T., Cai, J., Zhang, Y., Xia, S.T., and Zhang, L. (2019, January 15\u201320). Second-order attention network for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01132"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Magid, S.A., Zhang, Y., Wei, D., Jang, W.D., Lin, Z., Fu, Y., and Pfister, H. (2021, January 10\u201317). Dynamic high-pass filtering and multi-spectral attention for image super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00425"},{"key":"ref_85","unstructured":"Liu, D., Wen, B., Fan, Y., Loy, C.C., and Huang, T.S. (2018). Non-local recurrent network for image restoration. Adv. Neural Inf. Process. Syst., 31, Available online: http:\/\/s.dic.cool\/S\/tamTpxhq."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., Zhou, Y., Huang, L., Huang, T.S., and Shi, H. (2020, January 13\u201319). Image super-resolution with cross-scale non-local attention and exhaustive self-exemplars mining. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00573"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Wei, D., Qin, C., Wang, H., Pfister, H., and Fu, Y. (2021, January 10\u201317). Context reasoning attention network for image super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00424"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Mei, Y., Fan, Y., and Zhou, Y. (2021, January 20\u201325). Image super-resolution with non-local sparse attention. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00352"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Li, K., Hariharan, B., and Malik, J. (2016, January 27\u201330). Iterative instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.398"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Carreira, J., Agrawal, P., Fragkiadaki, K., and Malik, J. (2016, January 27\u201330). Human Pose Estimation with Iterative Error Feedback. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.512"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2018, January 18\u201321). Deep back-projection networks for super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00179"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Haris, M., Shakhnarovich, G., and Ukita, N. (2019, January 15\u201320). Recurrent back-projection network for video super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00402"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Li, Z., Yang, J., Liu, Z., Yang, X., Jeon, G., and Wu, W. (2019, January 15\u201320). Feedback Network for Image Super-Resolution. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00399"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Xie, W., Song, D., Xu, C., Xu, C., Zhang, H., and Wang, Y. (2021, January 10\u201317). Learning frequency-aware dynamic network for efficient super-resolution. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00427"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Kong, X., Zhao, H., Qiao, Y., and Dong, C. (2021, January 20\u201325). Classsr: A general framework to accelerate super-resolution networks by data characteristic. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01184"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3194","DOI":"10.1109\/TIP.2016.2564643","article-title":"Robust single image super-resolution via deep networks with sparse prior","volume":"25","author":"Liu","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Gao, X., and Xiong, H. (2016, January 25\u201328). A hybrid wavelet convolution network with sparse-coding for image super-resolution. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7532596"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"Wang, L., Dong, X., Wang, Y., Ying, X., Lin, Z., An, W., and Guo, Y. (2021, January 20\u201325). Exploring sparsity in image super-resolution for efficient inference. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00488"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Wang, Z., Lin, Z.L., and Qi, H. (2019, January 15\u201320). Image Super-Resolution by Neural Texture Transfer. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00817"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"MadhuMithraK, K., Ramanarayanan, S., Ram, K., and Sivaprakasam, M. (2021, January 13\u201316). Reference-Based Texture Transfer For Single Image Super-Resolution Of Magnetic Resonance Images. Proceedings of the 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Nice, France.","DOI":"10.1109\/ISBI48211.2021.9433961"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Yang, F., Yang, H., Fu, J., Lu, H., and Guo, B. (2020, January 13\u201319). Learning Texture Transformer Network for Image Super-Resolution. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00583"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Ledig, C., Theis, L., Husz\u00e1r, F., Caballero, J., Aitken, A.P., Tejani, A., Totz, J., Wang, Z., and Shi, W. (2017, January 21\u201326). Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.19"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","article-title":"A survey of regularization strategies for deep models","volume":"53","author":"Moradi","year":"2019","journal-title":"Artif. Intell. Rev."},{"key":"ref_104","unstructured":"Kuka\u010dka, J., Golkov, V., and Cremers, D. (2017). Regularization for Deep Learning: A Taxonomy. arXiv."},{"key":"ref_105","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_106","first-page":"7","article-title":"Improving neural networks with dropout","volume":"182","author":"Srivastava","year":"2013","journal-title":"Univ. Tor."},{"key":"ref_107","unstructured":"Konda, K.R., Bouthillier, X., Memisevic, R., and Vincent, P. (2015). Dropout as data augmentation. arXiv."},{"key":"ref_108","unstructured":"Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.R. (2012). Improving neural networks by preventing co-adaptation of feature detectors. arXiv."},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2012","journal-title":"Commun. ACM"},{"key":"ref_110","unstructured":"Li, M., Soltanolkotabi, M., and Oymak, S. (2020). Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks. arXiv."},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Prechelt, L. (1998). Early stopping-but when?. Neural Networks: Tricks of the Trade, Springer.","DOI":"10.1007\/3-540-49430-8_3"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Zoph, B., Cubuk, E.D., Ghiasi, G., Lin, T.Y., Shlens, J., and Le, Q.V. (2020, January 23\u201328). Learning data augmentation strategies for object detection. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58583-9_34"},{"key":"ref_113","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020, January 7\u20138). Random erasing data augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA."},{"key":"ref_114","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 7\u20139). Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning (PMLR), Lille, France."},{"key":"ref_115","unstructured":"Hinton, G.E., Vinyals, O., and Dean, J. (2015). Distilling the Knowledge in a Neural Network. arXiv."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Lee, W., Lee, J., Kim, D., and Ham, B. (2020). Learning with Privileged Information for Efficient Image Super-Resolution. arXiv.","DOI":"10.1007\/978-3-030-58586-0_28"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Chen, H., Chen, X., Deng, Y., Xu, C., and Wang, Y. (2021, January 20\u201325). Data-free knowledge distillation for image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00776"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, Y., Xu, C., Shi, B., Xu, C., Tian, Q., and Xu, C. (2020, January 13\u201319). AdderNet: Do we really need multiplications in deep learning?. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00154"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Song, D., Wang, Y., Chen, H., Xu, C., Xu, C., and Tao, D. (2021, January 20\u201325). Addersr: Towards energy efficient image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01539"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"Vaswani, A., Ramachandran, P., Srinivas, A., Parmar, N., Hechtman, B., and Shlens, J. (2021, January 20\u201325). Scaling local self-attention for parameter efficient visual backbones. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01270"},{"key":"ref_121","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 10\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_122","unstructured":"Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., and J\u00e9gou, H. (2021, January 18\u201324). Training data-efficient image transformers & distillation through attention. Proceedings of the International Conference on Machine Learning (PMLR), Virtual."},{"key":"ref_123","first-page":"9355","article-title":"Twins: Revisiting the design of spatial attention in vision transformers","volume":"34","author":"Chu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_124","first-page":"12116","article-title":"Do vision transformers see like convolutional neural networks?","volume":"34","author":"Raghu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Chen, X., Wang, X., Zhou, J., and Dong, C. (2022). Activating More Pixels in Image Super-Resolution Transformer. arXiv.","DOI":"10.1109\/CVPR52729.2023.02142"},{"key":"ref_126","doi-asserted-by":"crossref","unstructured":"Lu, Z., Li, J., Liu, H., Huang, C., Zhang, L., and Zeng, T. (2022, January 19\u201324). Transformer for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA.","DOI":"10.1109\/CVPRW56347.2022.00061"},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Liang, J., Cao, J., Sun, G., Zhang, K., Van Gool, L., and Timofte, R. (2021, January 10\u201317). Swinir: Image restoration using swin transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00210"},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Cai, Q., Qian, Y., Li, J., Lv, J., Yang, Y.H., Wu, F., and Zhang, D. (2022). HIPA: Hierarchical Patch Transformer for Single Image Super Resolution. arXiv.","DOI":"10.1109\/TIP.2023.3279977"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"9277","DOI":"10.1109\/TGRS.2019.2924818","article-title":"Remote Sensing Image Superresolution Using Deep Residual Channel Attention","volume":"57","author":"Haut","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"3512","DOI":"10.1109\/TGRS.2018.2885506","article-title":"Achieving Super-Resolution Remote Sensing Images via the Wavelet Transform Combined With the Recursive Res-Net","volume":"57","author":"Ma","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/JSTARS.2019.2925456","article-title":"Remote sensing image super-resolution using sparse representation and coupled sparse autoencoder","volume":"12","author":"Shao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Ma, W., Pan, Z., Yuan, F., and Lei, B. (2019). Super-resolution of remote sensing images via a dense residual generative adversarial network. Remote Sens., 11.","DOI":"10.3390\/rs11212578"},{"key":"ref_133","doi-asserted-by":"crossref","unstructured":"Dong, X., Xi, Z., Sun, X., and Gao, L. (2019). Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution. Remote Sens., 11.","DOI":"10.3390\/rs11232857"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"7918","DOI":"10.1109\/TGRS.2019.2917427","article-title":"Super-Resolution of Single Remote Sensing Image Based on Residual Dense Backprojection Networks","volume":"57","author":"Pan","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Dong, X., Xi, Z., Sun, X., and Yang, L. (October, January 26). Remote Sensing Image Super-Resolution via Enhanced Back-Projection Networks. Proceedings of the IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9323316"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"6048","DOI":"10.1080\/01431161.2021.1934598","article-title":"Super-resolution of remotely sensed data using channel attention based deep learning approach","volume":"42","author":"Wang","year":"2021","journal-title":"Int. J. Remote Sens."},{"key":"ref_137","first-page":"1","article-title":"FeNet: Feature Enhancement Network for Lightweight Remote-Sensing Image Super-Resolution","volume":"60","author":"Wang","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Huang, B., Guo, Z., Wu, L., He, B., Li, X., and Lin, Y. (2021). Pyramid Information Distillation Attention Network for Super-Resolution Reconstruction of Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13245143"},{"key":"ref_139","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xu, T., Li, J., Jiang, S., and Zhang, Y. (2022). Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling. Remote Sens., 14.","DOI":"10.3390\/rs14122895"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Yue, X., Chen, X., Zhang, W., Ma, H., Wang, L., Zhang, J., Wang, M., and Jiang, B. (2022). Super-Resolution Network for Remote Sensing Images via Preclassification and Deep\u2013Shallow Features Fusion. Remote Sens., 14.","DOI":"10.3390\/rs14040925"},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Xu, Y., Luo, W., Hu, A., Xie, Z., Xie, X., and Tao, L. (2022). TE-SAGAN: An Improved Generative Adversarial Network for Remote Sensing Super-Resolution Images. Remote Sens., 14.","DOI":"10.3390\/rs14102425"},{"key":"ref_142","doi-asserted-by":"crossref","unstructured":"Guo, M., Zhang, Z., Liu, H., and Huang, Y. (2022). NDSRGAN: A Novel Dense Generative Adversarial Network for Real Aerial Imagery Super-Resolution Reconstruction. Remote Sens., 14.","DOI":"10.3390\/rs14071574"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Qin, X., Gao, X., and Yue, K. (2018, January 5\u20137). Remote Sensing Image Super-Resolution using Multi-Scale Convolutional Neural Network. Proceedings of the 2018 11th UK-Europe-China Workshop on Millimeter Waves and Terahertz Technologies (UCMMT), Hangzhou, China.","DOI":"10.1109\/UCMMT45316.2018.9015801"},{"key":"ref_144","doi-asserted-by":"crossref","first-page":"5503","DOI":"10.1109\/TGRS.2020.2966669","article-title":"E-DBPN: Enhanced deep back-projection networks for remote sensing scene image superresolution","volume":"58","author":"Yu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"5183","DOI":"10.1109\/TGRS.2020.3009918","article-title":"Remote sensing image super-resolution via mixed high-order attention network","volume":"59","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"4764","DOI":"10.1109\/TGRS.2020.2966805","article-title":"Scene-adaptive remote sensing image super-resolution using a multiscale attention network","volume":"58","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_147","unstructured":"Arefin, M.R., Michalski, V., St-Charles, P.L., Kalaitzis, A., Kim, S., Kahou, S.E., and Bengio, Y. (2020, January 14\u201319). Multi-image super-resolution for remote sensing using deep recurrent networks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA."},{"key":"ref_148","first-page":"1","article-title":"Contextual Transformation Network for Lightweight Remote-Sensing Image Super-Resolution","volume":"60","author":"Wang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_149","first-page":"1","article-title":"U-Shaped Attention Connection Network for Remote-Sensing Image Super-Resolution","volume":"19","author":"Jiang","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"6792","DOI":"10.1109\/TGRS.2018.2843525","article-title":"A new deep generative network for unsupervised remote sensing single-image super-resolution","volume":"56","author":"Haut","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Wang, P., Zhang, H., Zhou, F., and Jiang, Z. (August, January 28). Unsupervised remote sensing image super-resolution using cycle CNN. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8898648"},{"key":"ref_152","doi-asserted-by":"crossref","first-page":"29027","DOI":"10.1109\/ACCESS.2020.2972300","article-title":"An unsupervised remote sensing single-image super-resolution method based on generative adversarial network","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2020.3038405","article-title":"A multi-degradation aided method for unsupervised remote sensing image super resolution with convolution neural networks","volume":"60","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5423\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:07Z","timestamp":1760144707000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/21\/5423"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,28]]},"references-count":153,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["rs14215423"],"URL":"https:\/\/doi.org\/10.3390\/rs14215423","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,28]]}}}