{"type":"ScholarlyArticle","title":"Low-Light Video Enhancement via Spatial-Temporal Consistent Decomposition","authors":[{"name":"Xiaogang Xu","slug":"xiaogang-xu","given":"Xiaogang","family":"Xu","affiliation":""},{"name":"Kun Zhou","slug":"kun-zhou","given":"Kun","family":"Zhou","affiliation":""},{"name":"Tao Hu","slug":"tao-hu","given":"Tao","family":"Hu","affiliation":""},{"name":"Jiafei Wu","slug":"jiafei-wu","given":"Jiafei","family":"Wu","affiliation":""},{"name":"Ruixing Wang","slug":"ruixing-wang","given":"Ruixing","family":"Wang","affiliation":""},{"name":"Hao Peng","slug":"hao-peng","given":"Hao","family":"Peng","affiliation":""},{"name":"Bei Yu","slug":"bei-yu","given":"Bei","family":"Yu","affiliation":""}],"abstract":"Low-Light Video Enhancement (LLVE) seeks to restore dynamic or static scenes plagued by severe invisibility and noise. In this paper, we present an innovative video decomposition strategy that incorporates view-independent and view-dependent components to enhance the performance of LLVE. We leverage dynamic cross-frame correspondences for the view-independent term (which primarily captures intrinsic appearance) and impose a scene-level continuity constraint on the view-dependent term (which mainly describes the shading condition) to achieve consistent and satisfactory decomposition results. To further ensure consistent decomposition, we introduce a dual-structure enhancement network featuring a cross-frame interaction mechanism. By supervising different frames simultaneously, this network encourages them to exhibit matching decomposition features. This mechanism can seamlessly integrate with encoder-decoder single-frame networks, incurring minimal additional parameter costs. Extensive experiments are conducted on widely recognized LLVE benchmarks, covering diverse scenarios. Our framework consistently outperforms existing methods, establishing a new SOTA performance.","doi":"10.24963/ijcai.2025/238","url":"https://ijcai.org/proceedings/2025/238","pdf":"https://ijcai.org/proceedings/2025/0238.pdf","markdown":"https://ijcai.org/proceedings/2025/238.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/238","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":238,"pages":{"first":2134,"last":2142},"keywords":["Computer Vision: CV: Computational photography","Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}