{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:14:41Z","timestamp":1760145281435,"version":"build-2065373602"},"reference-count":63,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"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":["52204177","52304182","2020QN49."],"award-info":[{"award-number":["52204177","52304182","2020QN49."]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["52204177","52304182","2020QN49."],"award-info":[{"award-number":["52204177","52304182","2020QN49."]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model\u2019s overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.<\/jats:p>","DOI":"10.3390\/s24144597","type":"journal-article","created":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T09:46:57Z","timestamp":1721123217000},"page":"4597","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Single Image Super-Resolution via Wide-Activation Feature Distillation Network"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-2393-3818","authenticated-orcid":false,"given":"Zhen","family":"Su","sequence":"first","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Science and Technology Bureau of Jining City, Shandong Province, Jining 272000, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1573-9880","authenticated-orcid":false,"given":"Yuze","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-9310-5257","authenticated-orcid":false,"given":"Xiang","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0197-5296","authenticated-orcid":false,"given":"Mang","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4475-845X","authenticated-orcid":false,"given":"Deqiang","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-8954-1162","authenticated-orcid":false,"given":"Chao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"State Grid Nanjing Automation Co., Ltd., Nanjing 210032, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3345-9665","authenticated-orcid":false,"given":"He","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102398","DOI":"10.1016\/j.aei.2024.102398","article-title":"Indicative Vision Transformer for end-to-end zero-shot sketch-based image retrieval","volume":"60","author":"Zhang","year":"2024","journal-title":"Adv. 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