{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T00:15:13Z","timestamp":1771632913456,"version":"3.50.1"},"reference-count":63,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T00:00:00Z","timestamp":1741737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of Chongqing (Innovation and Development Joint Fund)","award":["CSTB2023NSCQ-LZX0149"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0149"]}]},{"name":"Natural Science Foundation of Chongqing (Innovation and Development Joint Fund)","award":["CYB240031"],"award-info":[{"award-number":["CYB240031"]}]},{"name":"Natural Science Foundation of Chongqing (Innovation and Development Joint Fund)","award":["2023CDJKYJH019"],"award-info":[{"award-number":["2023CDJKYJH019"]}]},{"name":"Natural Science Foundation of Chongqing (Innovation and Development Joint Fund)","award":["62302068"],"award-info":[{"award-number":["62302068"]}]},{"name":"Graduate Student Research Innovation Project of Chongqing","award":["CSTB2023NSCQ-LZX0149"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0149"]}]},{"name":"Graduate Student Research Innovation Project of Chongqing","award":["CYB240031"],"award-info":[{"award-number":["CYB240031"]}]},{"name":"Graduate Student Research Innovation Project of Chongqing","award":["2023CDJKYJH019"],"award-info":[{"award-number":["2023CDJKYJH019"]}]},{"name":"Graduate Student Research Innovation Project of Chongqing","award":["62302068"],"award-info":[{"award-number":["62302068"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["CSTB2023NSCQ-LZX0149"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0149"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["CYB240031"],"award-info":[{"award-number":["CYB240031"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["2023CDJKYJH019"],"award-info":[{"award-number":["2023CDJKYJH019"]}]},{"name":"Fundamental Research Funds for the Central Universities","award":["62302068"],"award-info":[{"award-number":["62302068"]}]},{"name":"Natural Science Foundation of China","award":["CSTB2023NSCQ-LZX0149"],"award-info":[{"award-number":["CSTB2023NSCQ-LZX0149"]}]},{"name":"Natural Science Foundation of China","award":["CYB240031"],"award-info":[{"award-number":["CYB240031"]}]},{"name":"Natural Science Foundation of China","award":["2023CDJKYJH019"],"award-info":[{"award-number":["2023CDJKYJH019"]}]},{"name":"Natural Science Foundation of China","award":["62302068"],"award-info":[{"award-number":["62302068"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Effectively handling mixed noise types and varying intensities is crucial for accurate information extraction and analysis, particularly in resource-limited edge computing scenarios. Conventional image denoising approaches struggle with unseen noise distributions, limiting their effectiveness in real-world applications such as object detection, classification, and change detection. To address these challenges, we introduce a novel image denoising framework that integrates asymmetric learning with symmetric fusion. It leverages a pretrained model trained only on clean images to provide semantic priors, while a supervised module learns direct noise-to-clean mappings using paired noisy\u2013clean data. The asymmetry in our approach stems from its dual training objectives: a pretrained encoder extracts semantic priors from noise-free data, while a supervised module learns noise-to-clean mappings. The symmetry is achieved through a structured fusion of pretrained priors and supervised features, enhancing generalization across diverse noise distributions, including those in edge computing environments. Extensive evaluations across multiple noise types and intensities, including real-world remote sensing data, demonstrate the superior robustness of our approach. Our method achieves state-of-the-art performance in both in-distribution and out-of-distribution noise scenarios, significantly enhancing image quality for downstream tasks such as environmental monitoring and disaster response. Future work may explore extending this framework to specialized applications like hyperspectral imaging and nighttime analysis while further refining the interplay between symmetry and asymmetry in deep-learning-based image restoration.<\/jats:p>","DOI":"10.3390\/sym17030424","type":"journal-article","created":{"date-parts":[[2025,3,12]],"date-time":"2025-03-12T07:31:42Z","timestamp":1741764702000},"page":"424","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Asymmetric Training and Symmetric Fusion for Image Denoising in Edge Computing"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6196-2560","authenticated-orcid":false,"given":"Yupeng","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1932-3435","authenticated-orcid":false,"given":"Xiaofeng","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Computer Science, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Banjade, T.P., Zhou, C., Chen, H., Li, H., Deng, J., Zhou, F., and Adhikari, R. 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