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Single-image super resolution (SISR) is a technique to improve the spatial resolution of digital images and has attracted the attention of many researchers. In recent years, with the advancement of deep learning (DL) frameworks, many DL-based SISR models have been proposed and achieved state-of-the-art performance; however, most SISR models for RSIs use the bicubic downsampler to construct low-resolution (LR) and high-resolution (HR) training pairs. Considering that the quality of the actual RSIs depends on a variety of factors, such as illumination, atmosphere, imaging sensor responses, and signal processing, training on \u201cideal\u201d datasets results in a dramatic drop in model performance on real RSIs. To address this issue, we propose to build a more realistic training dataset by modeling the degradation with blur kernels and imaging noises. We also design a novel residual balanced attention network (RBAN) as a generator to estimate super-resolution results from the LR inputs. To encourage RBAN to generate more realistic textures, we apply a UNet-shape discriminator for adversarial training. Both referenced evaluations on synthetic data and non-referenced evaluations on actual images were carried out. Experimental results validate the effectiveness of the proposed framework, and our model exhibits state-of-the-art performance in quantitative evaluation and visual quality. We believe that the proposed framework can facilitate super-resolution techniques from research to practical applications in RSIs processing.<\/jats:p>","DOI":"10.3390\/rs14122895","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T11:45:44Z","timestamp":1655466344000},"page":"2895","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":42,"title":["Single-Image Super Resolution of Remote Sensing Images with Real-World Degradation Modeling"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1015-0534","authenticated-orcid":false,"given":"Jizhou","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingfa","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6936-9485","authenticated-orcid":false,"given":"Jianan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shenwang","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhan","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Optics and Photonics, Beijing Institute of Technology, Beijing 100081, China"},{"name":"Beijing Institute of Technology Chongqing Innovation Center, Chongqing 401120, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1304","DOI":"10.1109\/TGRS.2014.2337658","article-title":"Analysis of oblique aerial images for land cover and point cloud classification in an urban environment","volume":"53","author":"Rau","year":"2014","journal-title":"IEEE Trans. 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