{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T11:46:51Z","timestamp":1762429611090,"version":"build-2065373602"},"reference-count":67,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006285","name":"U.S. Department of Transportation, Federal Highway Administration","doi-asserted-by":"publisher","award":["693jj320c000023"],"award-info":[{"award-number":["693jj320c000023"]}],"id":[{"id":"10.13039\/100006285","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Images used in real-world applications such as image or video retrieval, outdoor surveillance, and autonomous driving suffer from poor weather conditions. When designing robust computer vision systems, removing adverse weather such as haze, rain, and snow is a significant problem. Recently, deep-learning methods offered a solution for a single type of degradation. Current state-of-the-art universal methods struggle with combinations of degradations, such as haze and rain streaks. Few algorithms have been developed that perform well when presented with images containing multiple adverse weather conditions. This work focuses on developing an efficient solution for multiple adverse weather removal, using a unified quaternion neural architecture called CMAWRNet. It is based on a novel texture\u2013structure decomposition block, a novel lightweight encoder\u2013decoder quaternion transformer architecture, and an attentive fusion block with low-light correction. We also introduce a quaternion similarity loss function to better preserve color information. The quantitative and qualitative evaluation of the current state-of-the-art benchmarking datasets and real-world images shows the performance advantages of the proposed CMAWRNet, compared to other state-of-the-art weather removal approaches dealing with multiple weather artifacts. Extensive computer simulations validate that CMAWRNet improves the performance of downstream applications, such as object detection. This is the first time the decomposition approach has been applied to the universal weather removal task.<\/jats:p>","DOI":"10.3390\/jimaging11110382","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T03:33:07Z","timestamp":1761881587000},"page":"382","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CMAWRNet: Multiple Adverse Weather Removal via a Unified Quaternion Neural Architecture"],"prefix":"10.3390","volume":"11","author":[{"given":"Vladimir","family":"Frants","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Staten Island, City University of New York, Staten Island, NY 10314, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karen","family":"Panetta","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Tufts University, Medford, MA 02155, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter","family":"Huang","sequence":"additional","affiliation":[{"name":"Turner\u2013Fairbank Highway Research Center, Federal Highway Administration (FHWA), U.S. Department of Transportation, McLean, VA 22101, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, R., Wang, Z., Guo, J., and Zhang, C. 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