{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T22:04:45Z","timestamp":1772575485191,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,12,26]],"date-time":"2020-12-26T00:00:00Z","timestamp":1608940800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2019YFE0127400"],"award-info":[{"award-number":["2019YFE0127400"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41671391, 41922043, 41871287"],"award-info":[{"award-number":["41671391, 41922043, 41871287"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Image enhancement (IE) technology can help enhance the brightness of remote-sensing images to obtain better interpretation and visualization effects. Convolutional neural networks (CNN), such as the Low-light CNN (LLCNN) and Super-resolution CNN (SRCNN), have achieved great success in image enhancement, image super resolution, and other image-processing applications. Therefore, we adopt CNN to propose a new neural network architecture with end-to-end strategy for low-light remote-sensing IE, named remote-sensing CNN (RSCNN). In RSCNN, an upsampling operator is adopted to help learn more multi-scaled features. With respect to the lack of labeled training data in remote-sensing image datasets for IE, we use real natural image patches to train firstly and then perform fine-tuning operations with simulated remote-sensing image pairs. Reasonably designed experiments are carried out, and the results quantitatively show the superiority of RSCNN in terms of structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) over conventional techniques for low-light remote-sensing IE. Furthermore, the results of our method have obvious qualitative advantages in denoising and maintaining the authenticity of colors and textures.<\/jats:p>","DOI":"10.3390\/rs13010062","type":"journal-article","created":{"date-parts":[[2020,12,27]],"date-time":"2020-12-27T20:52:21Z","timestamp":1609102341000},"page":"62","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":86,"title":["RSCNN: A CNN-Based Method to Enhance Low-Light Remote-Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0015-0570","authenticated-orcid":false,"given":"Linshu","family":"Hu","sequence":"first","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}]},{"given":"Mengjiao","family":"Qin","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1475-8480","authenticated-orcid":false,"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}]},{"given":"Zhenhong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Zhejiang Provincial Key Laboratory of Geographic Information Science, Hangzhou 310028, China"}]},{"given":"Renyi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Earth Sciences, Zhejiang University, Hangzhou 310027, China"},{"name":"Ocean Academy, Zhejiang University, Zhoushan 316021, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"875","DOI":"10.1038\/nclimate1908","article-title":"Dickinson, R. 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