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Specifically, classical super-resolution methods tend to amplify blur artifacts when increasing resolution, while deblurring methods may lose structural details during restoration. To overcome these limitations, this paper proposes a novel Structure Preservation and Interactive Fusion Network (SPIFNet) that integrates semantic priors from deep learning with edge-aware structural guidance. First, based on the prior knowledge that edge information is crucial for image super-resolution and deblurring tasks, a structure-preserving module based on edge maps is proposed. This module utilizes edge-aided information and multi-scale edge constraints to enhance the structural and detail preservation capabilities of the reconstruction network. Additionally, to obtain accurate edge information of blurry images for building the structure-preserving module, a multi-branch fusion-structured edge extraction network is introduced. Furthermore, to fully utilize the relationship between image deblurring and super-resolution tasks, an edge-guided interactive fusion module is proposed to adaptively fuse feature information from deblurring and super-resolution tasks. To improve the network\u2019s capacity to represent features, local and global attention methods are used. The suggested approach for super-resolution of fuzzy images has been rigorously validated by both quantitative and qualitative testing findings.<\/jats:p>","DOI":"10.1142\/s0218001425540199","type":"journal-article","created":{"date-parts":[[2025,9,27]],"date-time":"2025-09-27T04:28:46Z","timestamp":1758947326000},"source":"Crossref","is-referenced-by-count":0,"title":["SPIFNet: Structure Preservation and Interactive Fusion Network for Image Super-Resolution Restoration Algorithm"],"prefix":"10.1142","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6720-7271","authenticated-orcid":false,"given":"Guodong","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science, Shandong Xiehe University, Jinan 250107, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2121-4838","authenticated-orcid":false,"given":"Qianqian","family":"Li","sequence":"additional","affiliation":[{"name":"Sinopec Sales Co., Ltd., Shandong Binzhou Petroleum Branch, Binzhou 256600, P. R. China"}]}],"member":"219","published-online":{"date-parts":[[2025,10,31]]},"reference":[{"key":"S0218001425540199BIB001","doi-asserted-by":"publisher","DOI":"10.1007\/s00371-020-01998-z"},{"issue":"3","key":"S0218001425540199BIB002","first-page":"3154","volume":"45","author":"Chan K. C. K.","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"S0218001425540199BIB003","first-page":"1","volume":"1","author":"Chen Y.","year":"2023","journal-title":"Vis. 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