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The diagnostic accuracy, however, heavily relies on the clarity of subtle lesions, which can be significantly affected by image resolution. Achieving a balance between reconstruction quality, model complexity, and training efficiency remains a key challenge, particularly under limited data conditions. To address these issues, a lightweight end\u2010to\u2010end model LiteZSSR is proposed for super\u2010resolution reconstruction of fundus images, incorporating a residual information distillation module to extract multi\u2010scale features within a shallow network architecture, effectively retaining both local and global contextual information. In addition, a multi\u2010feature fusion group composed of multiple large kernel attention blocks is designed to strengthen feature representation while minimizing redundancy and computational overhead. Unsupervised training based on internal image learning is adopted to eliminate dependence on large\u2010scale datasets and to suppress artifacts commonly produced by CNN\u2010based SRR methods. Extensive experiments on publicly available fundus image datasets, including DRIVE, STARE, and CHASEDB1, demonstrate that LiteZSSR outperforms existing state\u2010of\u2010the\u2010art methods in terms of PSNR and SSIM, while significantly reducing model parameters. These results highlight its potential for practical deployment in clinical fundus image enhancement\u00a0tasks.<\/jats:p>","DOI":"10.1049\/ipr2.70178","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T09:18:28Z","timestamp":1754903908000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Lightweight Zero\u2010Shot Superresolution Reconstruction of Fundus Images Based on Residual Information Distillation and Multi\u2010Feature Fusion"],"prefix":"10.1049","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7889-6263","authenticated-orcid":false,"given":"Xiaoxin","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China"},{"name":"College of Computer Science and Technology Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangqi","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weijia","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Software Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yihuan","family":"Wei","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengyang","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Software Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongliang","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology Jilin University Changchun China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Songtian","family":"Che","sequence":"additional","affiliation":[{"name":"Ophthalmology Department Bethune Second Hospital of Jilin University Changchun Jilin China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.101971"},{"issue":"4","key":"e_1_2_10_3_1","first-page":"661","article-title":"Advanced Diabetic Retinopathy Detection With the R\u2010CNN: A Unified Visual Health Solution","volume":"34","author":"Srinivasu V. 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