{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,17]],"date-time":"2026-02-17T11:32:44Z","timestamp":1771327964353,"version":"3.50.1"},"reference-count":47,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T00:00:00Z","timestamp":1707177600000},"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":["62373372"],"award-info":[{"award-number":["62373372"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272485"],"award-info":[{"award-number":["62272485"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Yz2022065"],"award-info":[{"award-number":["Yz2022065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["202310489005"],"award-info":[{"award-number":["202310489005"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Undergraduate Training Program for Innovation and Entrepreneurship at Yangtze University","award":["62373372"],"award-info":[{"award-number":["62373372"]}]},{"name":"Undergraduate Training Program for Innovation and Entrepreneurship at Yangtze University","award":["62272485"],"award-info":[{"award-number":["62272485"]}]},{"name":"Undergraduate Training Program for Innovation and Entrepreneurship at Yangtze University","award":["Yz2022065"],"award-info":[{"award-number":["Yz2022065"]}]},{"name":"Undergraduate Training Program for Innovation and Entrepreneurship at Yangtze University","award":["202310489005"],"award-info":[{"award-number":["202310489005"]}]},{"name":"National Innovation and Entrepreneurship Training Program for College Students","award":["62373372"],"award-info":[{"award-number":["62373372"]}]},{"name":"National Innovation and Entrepreneurship Training Program for College Students","award":["62272485"],"award-info":[{"award-number":["62272485"]}]},{"name":"National Innovation and Entrepreneurship Training Program for College Students","award":["Yz2022065"],"award-info":[{"award-number":["Yz2022065"]}]},{"name":"National Innovation and Entrepreneurship Training Program for College Students","award":["202310489005"],"award-info":[{"award-number":["202310489005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the development of image super-resolution (SR) has explored the capabilities of convolutional neural networks (CNNs). The current research tends to use deeper CNNs to improve performance. However, blindly increasing the depth of the network does not effectively enhance its performance. Moreover, as the network depth increases, more issues arise during the training process, requiring additional training techniques. In this paper, we propose a lightweight image super-resolution reconstruction algorithm (SISR-RFDM) based on the residual feature distillation mechanism (RFDM). Building upon residual blocks, we introduce spatial attention (SA) modules to provide more informative cues for recovering high-frequency details such as image edges and textures. Additionally, the output of each residual block is utilized as hierarchical features for global feature fusion (GFF), enhancing inter-layer information flow and feature reuse. Finally, all these features are fed into the reconstruction module to restore high-quality images. Experimental results demonstrate that our proposed algorithm outperforms other comparative algorithms in terms of both subjective visual effects and objective evaluation quality. The peak signal-to-noise ratio (PSNR) is improved by 0.23 dB, and the structural similarity index (SSIM) reaches 0.9607.<\/jats:p>","DOI":"10.3390\/s24041049","type":"journal-article","created":{"date-parts":[[2024,2,6]],"date-time":"2024-02-06T03:22:31Z","timestamp":1707189751000},"page":"1049","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Lightweight Image Super-Resolution Reconstruction Algorithm Based on the Residual Feature Distillation Mechanism"],"prefix":"10.3390","volume":"24","author":[{"given":"Zihan","family":"Yu","sequence":"first","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7339-3130","authenticated-orcid":false,"given":"Chang","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Yangtze University, Jingzhou 434023, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianbiao","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electronic Information, Central South University, Changsha 410083, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,6]]},"reference":[{"key":"ref_1","first-page":"700","article-title":"A High-precision Water Segmentation Algorithm for SAR Image and its Application","volume":"43","author":"Chen","year":"2021","journal-title":"J. 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