{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T07:41:49Z","timestamp":1761896509822,"version":"build-2065373602"},"reference-count":29,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2017,5,7]],"date-time":"2017-05-07T00:00:00Z","timestamp":1494115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Super-resolution (SR) image reconstruction is a technique used to recover a high-resolution image using the cumulative information provided by several low-resolution images. With the help of SR techniques, satellite remotely sensed images can be combined to achieve a higher-resolution image, which is especially useful for a two- or three-line camera satellite, e.g., the ZY-3 high-resolution Three Line Camera (TLC) satellite. In this paper, we introduce the application of the SR reconstruction method, including motion estimation and the robust super-resolution technique, to ZY-3 TLC images. The results show that SR reconstruction can significantly improve both the resolution and image quality of ZY-3 TLC images.<\/jats:p>","DOI":"10.3390\/s17051062","type":"journal-article","created":{"date-parts":[[2017,5,8]],"date-time":"2017-05-08T11:45:16Z","timestamp":1494243916000},"page":"1062","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Super-Resolution Reconstruction of High-Resolution Satellite ZY-3 TLC Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2034-982X","authenticated-orcid":false,"given":"Lin","family":"Li","sequence":"first","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]},{"given":"Heng","family":"Luo","sequence":"additional","affiliation":[{"name":"Geomatics Center of Guangxi, Guangxi Bureau of Surveying, Mapping and GeoInformation, Nanning 530023, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8066-9203","authenticated-orcid":false,"given":"Shen","family":"Ying","sequence":"additional","affiliation":[{"name":"School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,5,7]]},"reference":[{"key":"ref_1","first-page":"317","article-title":"Multiframe image restoration and registration","volume":"Volume 1","author":"Tsai","year":"1984","journal-title":"Advances in Computer Vision and Image Processing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"364","DOI":"10.1016\/j.imavis.2008.05.010","article-title":"A soft MAP framework for blind super-resolution image reconstruction","volume":"27","author":"He","year":"2009","journal-title":"Image Vis. 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