{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T15:51:18Z","timestamp":1783612278802,"version":"3.55.0"},"reference-count":33,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T00:00:00Z","timestamp":1740355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Development Plan Project of Weifang City","award":["2023GX009"],"award-info":[{"award-number":["2023GX009"]}]},{"name":"Science and Technology Development Plan Project of Weifang City","award":["23KJA520006"],"award-info":[{"award-number":["23KJA520006"]}]},{"name":"Major Project of Natural Science Fundamental Research of the Jiangsu Higher Education Institutions of China","award":["2023GX009"],"award-info":[{"award-number":["2023GX009"]}]},{"name":"Major Project of Natural Science Fundamental Research of the Jiangsu Higher Education Institutions of China","award":["23KJA520006"],"award-info":[{"award-number":["23KJA520006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Existing methods have problems such as loss of details and insufficient reconstruction effect when processing complex images. To improve the quality and efficiency of image super-resolution reconstruction, this study proposes an improved algorithm based on super-resolution generative adversarial network and Swin Transformer. Firstly, on the ground of the traditional super-resolution generative adversarial network, combined with the global feature extraction capability of Swin Transformer, the model\u2019s capacity to capture multi-scale features and restore details is enhanced. Subsequently, by utilizing adversarial loss and perceptual loss to further optimize the training process, the image\u2019s visual quality is improved. The results show that the optimization algorithm had high PSNR and structural similarity index values in multiple benchmark test datasets, with the highest reaching 43.81 and 0.94, respectively, which are significantly better than the comparison algorithm. In practical applications, this algorithm demonstrated higher reconstruction accuracy and efficiency when reconstructing images with complex textures and rich edge details. The highest reconstruction accuracy could reach 98.03%, and the reconstruction time was as low as 0.2 s or less. In summary, this model can greatly improve the visual quality of image super-resolution reconstruction, better restore details, reduce detail loss, and provide an efficient and reliable solution for image super-resolution reconstruction tasks.<\/jats:p>","DOI":"10.3390\/sym17030337","type":"journal-article","created":{"date-parts":[[2025,2,24]],"date-time":"2025-02-24T07:46:57Z","timestamp":1740383217000},"page":"337","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Image Super-Resolution Reconstruction Algorithm Based on SRGAN and Swin Transformer"],"prefix":"10.3390","volume":"17","author":[{"given":"Chuilian","family":"Sun","sequence":"first","affiliation":[{"name":"Department of Discipline Construction and Graduate Education, Weifang University, Weifang 261061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6873-1075","authenticated-orcid":false,"given":"Chunmeng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Engineering, Jinling Institute of Technology, Nanjing 211169, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"He","sequence":"additional","affiliation":[{"name":"College of Communication, Weifang University, Weifang 261061, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"55921","DOI":"10.1007\/s11042-023-17660-4","article-title":"A review of single image super-resolution reconstruction based on deep learning","volume":"83","author":"Yu","year":"2024","journal-title":"Multimed. 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