{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:31:18Z","timestamp":1760149878560,"version":"build-2065373602"},"reference-count":54,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T00:00:00Z","timestamp":1695772800000},"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":["42071431","2022YFB4500800","2020YFB1805400"],"award-info":[{"award-number":["42071431","2022YFB4500800","2020YFB1805400"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["42071431","2022YFB4500800","2020YFB1805400"],"award-info":[{"award-number":["42071431","2022YFB4500800","2020YFB1805400"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Images taken in various real-world scenarios meet the symmetrical goal of simultaneously removing foreground rain-induced occlusions and restoring the background details. This inspires us to remember the principle of symmetry; real-world rain is a mixture of rain streaks and rainy haze and degrades the visual quality of the background. Current efforts formulate image rain streak removal and rainy haze removal as separate models, which disrupts the symmetrical characteristics of real-world rain and background, leading to significant performance degradation. To achieve this symmetrical balance, we propose a novel semisupervised coarse-to-fine guided generative adversarial network (Semi-RainGAN) for the mixture of rain removal. Beyond existing wisdom, Semi-RainGAN is a joint learning paradigm of the mixture of rain removal and attention and depth estimation. Additionally, it introduces a coarse-to-fine guidance mechanism that effectively fuses estimated image, attention, and depth features. This mechanism enables us to achieve symmetrically high-quality rain removal while preserving fine-grained details. To bridge the gap between synthetic and real-world rain, Semi-RainGAN makes full use of unpaired real-world rainy and clean images, enhancing its generalization to real-world scenarios. Extensive experiments on both synthetic and real-world rain datasets demonstrate clear visual and numerical improvements of Semi-RainGAN over sixteen state-of-the-art models.<\/jats:p>","DOI":"10.3390\/sym15101832","type":"journal-article","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T07:38:46Z","timestamp":1695800326000},"page":"1832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semi-RainGAN: A Semisupervised Coarse-to-Fine Guided Generative Adversarial Network for Mixture of Rain Removal"],"prefix":"10.3390","volume":"15","author":[{"given":"Rongwei","family":"Yu","sequence":"first","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8635-9863","authenticated-orcid":false,"given":"Ni","family":"Shu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430070, China"}]},{"given":"Peihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430070, China"}]},{"given":"Yizhan","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan 430070, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/0600000079","article-title":"Computer vision for autonomous vehicles: Problems, datasets and state of the art","volume":"Volume 12","author":"Janai","year":"2020","journal-title":"Foundations and Trends\u00ae in Computer Graphics and Vision"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TITS.2011.2119372","article-title":"A review of computer vision techniques for the analysis of urban traffic","volume":"12","author":"Buch","year":"2011","journal-title":"IEEE Trans. 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