{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:15:03Z","timestamp":1760242503290,"version":"build-2065373602"},"reference-count":28,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2017,9,18]],"date-time":"2017-09-18T00:00:00Z","timestamp":1505692800000},"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>Gaofen-4 is China\u2019s first geosynchronous orbit high-definition optical imaging satellite with extremely high temporal resolution. The features of staring imaging and high temporal resolution enable the super-resolution of multiple images of the same scene. In this paper, we propose a super-resolution (SR) technique to reconstruct a higher-resolution image from multiple low-resolution (LR) satellite images. The method first performs image registration in both the spatial and range domains. Then the point spread function (PSF) of LR images is parameterized by a Gaussian function and estimated by a blind deconvolution algorithm based on the maximum a posteriori (MAP). Finally, the high-resolution (HR) image is reconstructed by a MAP-based SR algorithm. The MAP cost function includes a data fidelity term and a regularized term. The data fidelity term is in the L2 norm, and the regularized term employs the Huber-Markov prior which can reduce the noise and artifacts while preserving the image edges. Experiments with real Gaofen-4 images show that the reconstructed images are sharper and contain more details than Google Earth ones.<\/jats:p>","DOI":"10.3390\/s17092142","type":"journal-article","created":{"date-parts":[[2017,9,19]],"date-time":"2017-09-19T11:04:35Z","timestamp":1505819075000},"page":"2142","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Multi-Frame Super-Resolution of Gaofen-4 Remote Sensing Images"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3243-9507","authenticated-orcid":false,"given":"Jieping","family":"Xu","sequence":"first","affiliation":[{"name":"National University of Defense Technology, College of Opto-Electronic Science and Engineering, Deya Road 109, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghui","family":"Liang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, College of Opto-Electronic Science and Engineering, Deya Road 109, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Liu","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, College of Opto-Electronic Science and Engineering, Deya Road 109, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongfu","family":"Huang","sequence":"additional","affiliation":[{"name":"National University of Defense Technology, College of Opto-Electronic Science and Engineering, Deya Road 109, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1109\/MSP.2003.1203207","article-title":"Super-resolution image reconstruction: a technical overview","volume":"20","author":"Park","year":"2003","journal-title":"IEEE Signal Process. 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