{"type":"ScholarlyArticle","title":"Learning to Remove Coupled Rain and Mist from Single Degradation Priors","authors":[{"name":"Yan Zhang","slug":"yan-zhang","given":"Yan","family":"Zhang","affiliation":""},{"name":"Yuxin Feng","slug":"yuxin-feng","given":"Yuxin","family":"Feng","affiliation":""},{"name":"Zhe Huang","slug":"zhe-huang","given":"Zhe","family":"Huang","affiliation":""},{"name":"Fan Zhou","slug":"fan-zhou","given":"Fan","family":"Zhou","affiliation":""},{"name":"Zhuo Su","slug":"zhuo-su","given":"Zhuo","family":"Su","affiliation":""}],"abstract":"Existing image deraining models struggle to handle complex real-world scenarios with rain-mist coupled degradation. The core challenge stems from the high visual similarity and spatial coupling between rain streaks and mist, making it difficult to accurately model their joint degradation patterns. Consequently, both specialized deraining models and all-in-one models for multiple degradations are ineffective in rain-mist coexistence scenarios. To address these challenges, we propose a novel Rain-Mist Removal (RMR) framework. It effectively utilizes single degradation priors from existing deraining and dehazing datasets to model the joint degradation, thereby achieving effective rain streak removal while preserving background structures obscured by mist. To enhance the generalization to real-world scenarios, we leverage text prompts trained in the CLIP perceptual space to drive the generated results toward real samples. Extensive experiments demonstrate that the proposed RMR outperforms state-of-the-art methods in rain-mist coexistence scenarios.","doi":"10.24963/ijcai.2026/232","url":"https://ijcai.org/proceedings/2026/232","pdf":"https://ijcai.org/proceedings/2026/0232.pdf","markdown":"https://ijcai.org/proceedings/2026/232.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/232","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Computer Vision","track":"Main Track","number":232,"pages":{"first":2082,"last":2090},"keywords":["Computer Vision: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}