{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T23:35:16Z","timestamp":1761176116501,"version":"build-2065373602"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686318","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:p>Improving the visual quality of rainy driving scenes poses a significant challenge, as the rain streaks in the distance and the raindrops attached to nearby surfaces exhibit different characteristics under varying lighting conditions, both during the daytime and nighttime. We note that existing image deraining approaches are trained independently for specific types of rain degradation, which limits the model\u2019s ability to adapt to dynamic driving scenes. In this paper, we introduce a new task: all-time rainy driving scene reconstruction, which aims to simultaneously address both daytime and nighttime rain degradation using a universal mix-trained model. Firstly, we construct a high-quality benchmark dataset termed RainDrive-10K, which contains four patterns: daytime rain streak, daytime raindrop, nighttime rain streak and nighttime raindrop. Furthermore, we also develop an effective Mamba-based baseline de-raining model, which employs a multi-patch progressive learning strategy to better help image restoration. Unlike existing Mamba-based methods that use fixed-scale scanning for feature extraction, we design a new multi-patch hierarchical scanning block that improves the model\u2019s robustness to diverse rain appearances. Extensive experiments demonstrate the effectiveness of our proposed model, and show that it achieves favorable performance against state-of-the-art ones. The dataset is available at https:\/\/github.com\/ZXXaaaa\/MP-RainMamba.<\/jats:p>","DOI":"10.3233\/faia250801","type":"book-chapter","created":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:40Z","timestamp":1761126160000},"source":"Crossref","is-referenced-by-count":0,"title":["A Multi-Degradation Dataset and a Universal Method for Image Deraining in All-Time Driving Scenes"],"prefix":"10.3233","author":[{"given":"Yiming","family":"Zheng","sequence":"first","affiliation":[{"name":"Xi\u2019an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yonghong","family":"Song","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinyue","family":"Su","sequence":"additional","affiliation":[{"name":"Xi\u2019an Jiaotong University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoyun","family":"Yang","sequence":"additional","affiliation":[{"name":"Beijing Jiaxun Feihong Electrical Co., Ltd"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","ECAI 2025"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA250801","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T09:42:40Z","timestamp":1761126160000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA250801"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"ISBN":["9781643686318"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia250801","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,21]]}}}