{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:03:13Z","timestamp":1760148193179,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,4,9]],"date-time":"2023-04-09T00:00:00Z","timestamp":1680998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2006228","61972241","23692106700","22ZR1427100"],"award-info":[{"award-number":["U2006228","61972241","23692106700","22ZR1427100"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Shanghai Soft Science Research Project","award":["U2006228","61972241","23692106700","22ZR1427100"],"award-info":[{"award-number":["U2006228","61972241","23692106700","22ZR1427100"]}]},{"name":"Natural Science Foundation of Shanghai","award":["U2006228","61972241","23692106700","22ZR1427100"],"award-info":[{"award-number":["U2006228","61972241","23692106700","22ZR1427100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Many Symmetry blocks were proposed in the Single Image Super-Resolution (SISR) task. The Attention-based block is powerful but costly on non-local features, while the Convolutional-based block is good at efficiently handling the local features. However, assembling two different Symmetry blocks will generate an Asymmetry block, making the classic Symmetry-block-based Super-Resolution (SR) architecture fail to deal with these Asymmetry blocks. In this paper, we proposed a new Dynamic fusion of Local and Non-local features-based Feedback Network (DLNFN) for SR, which focus on optimizing the traditional Symmetry-block-based SR architecture to hold two Symmetry blocks in parallel, making two Symmetry-blocks working on what they do best. (1) We introduce the Convolutional-based block for the local features and Attention-based network block for non-local features and propose the Delivery\u2013Adjust\u2013Fusion framework to hold these blocks. (2) we propose a Dynamic Weight block (DW block) which can generate different weight values to fuse the outputs on different feedback iterations. (3) We introduce the MAConv layer to optimize the In block, which is critical for our two blocks-based feedback algorithm. Experiments show our proposed DLNFN can take full advantage of two different blocks and outperform other state-of-the-art algorithms.<\/jats:p>","DOI":"10.3390\/sym15040885","type":"journal-article","created":{"date-parts":[[2023,4,10]],"date-time":"2023-04-10T03:49:29Z","timestamp":1681098569000},"page":"885","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["A Dynamic Fusion of Local and Non-Local Features-Based Feedback Network on Super-Resolution"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6376-8963","authenticated-orcid":false,"given":"Yuhao","family":"Liu","sequence":"first","affiliation":[{"name":"College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenzhong","family":"Chu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1109\/TPAMI.2020.2982166","article-title":"Deep Learning for Image Super-resolution: A Survey","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. 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