{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T10:55:15Z","timestamp":1769252115472,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2023,3,29]],"date-time":"2023-03-29T00:00:00Z","timestamp":1680048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62071466"],"award-info":[{"award-number":["62071466"]}]},{"name":"National Natural Science Foundation of China","award":["6142A010402"],"award-info":[{"award-number":["6142A010402"]}]},{"name":"National Natural Science Foundation of China","award":["2018GXNSFBA281086"],"award-info":[{"award-number":["2018GXNSFBA281086"]}]},{"name":"Beijing Research Institute of Uranium Geology","award":["62071466"],"award-info":[{"award-number":["62071466"]}]},{"name":"Beijing Research Institute of Uranium Geology","award":["6142A010402"],"award-info":[{"award-number":["6142A010402"]}]},{"name":"Beijing Research Institute of Uranium Geology","award":["2018GXNSFBA281086"],"award-info":[{"award-number":["2018GXNSFBA281086"]}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["62071466"],"award-info":[{"award-number":["62071466"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["6142A010402"],"award-info":[{"award-number":["6142A010402"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004607","name":"Guangxi Natural Science Foundation","doi-asserted-by":"publisher","award":["2018GXNSFBA281086"],"award-info":[{"award-number":["2018GXNSFBA281086"]}],"id":[{"id":"10.13039\/501100004607","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The advancements in image super-resolution technology have led to its widespread use in remote sensing applications. However, there is currently a lack of a general solution for the reconstruction of satellite images at arbitrary resolutions. The existing scale-arbitrary super-resolution methods are primarily predicated on learning either a discrete representation (DR) or a continuous representation (CR) of the image, with DR retaining the sensitivity to resolution and CR guaranteeing the generalization of the model. In this paper, we propose a novel image representation that combines the discrete and continuous representation, known as CDCR, which enables the extension of images to any desired resolution in a plug-and-play manner. CDCR consists of two components: a CR-based dense prediction that gathers more available information and a DR-based resolution-specific refinement that adjusts the predicted values of local pixels. Furthermore, we introduce a scale cumulative ascent (SCA) method, which enhances the performance of the dense prediction and improves the accuracy of the generated images at ultra-high magnifications. The efficacy and dependability of CDCR are substantiated by extensive experiments conducted on multiple remote sensing datasets, providing strong support for scenarios that require accurate images.<\/jats:p>","DOI":"10.3390\/rs15071827","type":"journal-article","created":{"date-parts":[[2023,3,30]],"date-time":"2023-03-30T01:05:26Z","timestamp":1680138326000},"page":"1827","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Combining Discrete and Continuous Representation: Scale-Arbitrary Super-Resolution for Satellite Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9220-2560","authenticated-orcid":false,"given":"Tai","family":"An","sequence":"first","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Chunlei","family":"Huo","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China"},{"name":"School of Automation and Electrical Engineering, University of Science and Technology, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2089-9733","authenticated-orcid":false,"given":"Shiming","family":"Xiang","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 101408, China"}]},{"given":"Chunhong","family":"Pan","sequence":"additional","affiliation":[{"name":"National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, X., Mu, H., Zhang, X., Wang, Z., Tan, T., and Sun, J. 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