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The test results on the Set5, Set14, Bsd100, and Urban100 datasets show that the PSNR and SSIM of our model are superior to most current algorithms, especially in the case of [Formula: see text] reconstruction results. PSNR has improved by 0.2 dB on the Set5 and Bsd100 datasets, and the algorithm has a better subjective visual effect. <\/jats:p>","DOI":"10.1142\/s0218001424540156","type":"journal-article","created":{"date-parts":[[2024,9,13]],"date-time":"2024-09-13T09:56:27Z","timestamp":1726221387000},"source":"Crossref","is-referenced-by-count":0,"title":["Image Super-Resolution Reconstruction Based on Dense Residual Attention and Multi-Scale Feature Fusion"],"prefix":"10.1142","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1591-2320","authenticated-orcid":false,"given":"Jianguo","family":"Shi","sequence":"first","affiliation":[{"name":"School of Computer and Information, Anhui Polytechnic University Wuhu, Anhui 241000, P. R. 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