{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T16:03:35Z","timestamp":1771517015504,"version":"3.50.1"},"reference-count":54,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Natural Science Foundation of China","award":["32472000"],"award-info":[{"award-number":["32472000"]}]},{"name":"the National Natural Science Foundation of China","award":["2024ZXDXA14"],"award-info":[{"award-number":["2024ZXDXA14"]}]},{"name":"the National Natural Science Foundation of China","award":["3072024LJ0404"],"award-info":[{"award-number":["3072024LJ0404"]}]},{"name":"the Key Research and Development Program of Heilongjiang Province","award":["32472000"],"award-info":[{"award-number":["32472000"]}]},{"name":"the Key Research and Development Program of Heilongjiang Province","award":["2024ZXDXA14"],"award-info":[{"award-number":["2024ZXDXA14"]}]},{"name":"the Key Research and Development Program of Heilongjiang Province","award":["3072024LJ0404"],"award-info":[{"award-number":["3072024LJ0404"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["32472000"],"award-info":[{"award-number":["32472000"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["2024ZXDXA14"],"award-info":[{"award-number":["2024ZXDXA14"]}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["3072024LJ0404"],"award-info":[{"award-number":["3072024LJ0404"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Single-image super-resolution (SISR) plays a critical role in enhancing visual quality for real-world applications, including industrial inspection and embedded vision systems. While deep learning-based approaches have made significant progress in SR, existing lightweight SR models often fail to accurately reconstruct high-frequency textures, especially under complex degradation scenarios, resulting in blurry edges and structural artifacts. To address this challenge, we propose a Dense Residual Fused Attention Network (DRFAN), a novel lightweight hybrid architecture designed to enhance high-frequency texture recovery in challenging degradation conditions. Moreover, by coupling convolutional layers and attention mechanisms through gated interaction modules, the DRFAN enhances local details and global dependencies with linear computational complexity, enabling the efficient utilization of multi-level spatial information while effectively alleviating the loss of high-frequency texture details. To evaluate its effectiveness, we conducted \u00d74 super-resolution experiments on five public benchmarks. The DRFAN achieves the best performance among all compared lightweight models. Visual comparisons show that the DRFAN restores more accurate geometric structures, with up to +1.2 dB\/+0.0281 SSIM gain over SwinIR-S on Urban100 samples. Additionally, on a domain-specific rice grain dataset, the DRFAN outperforms SwinIR-S by +0.19 dB in PSNR and +0.0015 in SSIM, restoring clearer textures and grain boundaries essential for industrial quality inspection. The proposed method provides a compelling balance between model complexity and image reconstruction fidelity, making it well-suited for deployment in resource-constrained visual systems and industrial applications.<\/jats:p>","DOI":"10.3390\/a18080454","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T11:44:45Z","timestamp":1753184685000},"page":"454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["DRFAN: A Lightweight Hybrid Attention Network for High-Fidelity Image Super-Resolution in Visual Inspection Applications"],"prefix":"10.3390","volume":"18","author":[{"given":"Ze-Long","family":"Li","sequence":"first","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bai","family":"Jiang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Liang","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4881-4378","authenticated-orcid":false,"given":"Zi-Teng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5350-098X","authenticated-orcid":false,"given":"Bin","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Si-Ye","family":"Jia","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong-Dan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2135-1892","authenticated-orcid":false,"given":"Bing","family":"Li","sequence":"additional","affiliation":[{"name":"College of Intelligent Science and Engineering, Harbin Engineering University, Harbin 150001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2273","DOI":"10.1049\/iet-ipr.2019.1438","article-title":"Survey of single image super\u2014Resolution reconstruction","volume":"14","author":"Li","year":"2020","journal-title":"IET Image Process."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., and Ghayvat, H. 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