{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T16:04:40Z","timestamp":1772813080172,"version":"3.50.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686547","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T00:00:00Z","timestamp":1772582400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,3,4]]},"abstract":"<jats:p>To address the issues of insufficient high-frequency detail reconstruction, texture distortion, and training instability in traditional Super-Resolution Generative Adversarial Network (SRGAN), this paper proposes an improved single-image super-resolution method based on coordinate attention and multi-domain loss. The improved generator introduces a lightweight coordinate attention mechanism in the residual module to enhance spatial feature selectivity. In the loss function, frequency-domain loss and least squares loss are added to simultaneously improve perceptual quality and pixel accuracy. Experimental results show that, on the Set5, Set14, BSD100, and Urban100 datasets, the proposed model significantly improves the average peak signal-to-noise ratio and structural similarity index compared to the original SRGAN, validating the effectiveness of the proposed method.<\/jats:p>","DOI":"10.3233\/faia260007","type":"book-chapter","created":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:26Z","timestamp":1772792426000},"source":"Crossref","is-referenced-by-count":0,"title":["Research on Single Image Super-Resolution Enhancement Based on Coordinate Attention and Multi-Domain Loss Function"],"prefix":"10.3233","author":[{"given":"Shengjie","family":"Li","sequence":"first","affiliation":[{"name":"School of Information Engineering, Nantong Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-6986-4805","authenticated-orcid":false,"given":"Hao","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nantong Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuancheng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nantong Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingtong","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nantong Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenqing","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nantong Institute of Technology, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Machine Learning and Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA260007","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T10:20:26Z","timestamp":1772792426000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA260007"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,4]]},"ISBN":["9781643686547"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia260007","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,4]]}}}