{"type":"ScholarlyArticle","title":"Dynamic Brightness Adaptation for Robust Multi-modal Image Fusion","authors":[{"name":"Yiming Sun","slug":"yiming-sun","given":"Yiming","family":"Sun","affiliation":"Tianjin University Tianjin Key Lab of Machine Learning Haihe Lab of ITAI Engineering Research Center of the Ministry of Education on Urban Intelligence and Digital Governance"},{"name":"Bing Cao","slug":"bing-cao","given":"Bing","family":"Cao","affiliation":"Tianjin University Tianjin Key Lab of Machine Learning Haihe Lab of ITAI Engineering Research Center of the Ministry of Education on Urban Intelligence and Digital Governance"},{"name":"Pengfei Zhu","slug":"pengfei-zhu","given":"Pengfei","family":"Zhu","affiliation":"Tianjin University Tianjin Key Lab of Machine Learning Haihe Lab of ITAI Engineering Research Center of the Ministry of Education on Urban Intelligence and Digital Governance"},{"name":"Qinghua Hu","slug":"qinghua-hu","given":"Qinghua","family":"Hu","affiliation":"Tianjin University Tianjin Key Lab of Machine Learning Haihe Lab of ITAI Engineering Research Center of the Ministry of Education on Urban Intelligence and Digital Governance"}],"abstract":"Infrared and visible image fusion aim to integrate modality strengths for visually enhanced, informative images. Visible imaging in real-world scenarios is susceptible to dynamic environmental brightness fluctuations, leading to texture degradation. Existing fusion methods lack robustness against such brightness perturbations, significantly compromising the visual fidelity of the fused imagery. To address this challenge, we propose the Brightness Adaptive multimodal dynamic fusion framework (BA-Fusion), which achieves robust image fusion despite dynamic brightness fluctuations. Specifically, we introduce a Brightness Adaptive Gate (BAG) module, which is designed to dynamically select features from brightness-related channels for normalization, while preserving brightness-independent structural information within the source images. Furthermore, we propose a brightness consistency loss function to optimize the BAG module. The entire framework is tuned via alternating training strategies. Extensive experiments validate that our method surpasses state-of-the-art methods in preserving multi-modal image information and visual fidelity, while exhibiting remarkable robustness across varying brightness levels. Our code is available: https://github.com/SunYM2020/BA-Fusion.","doi":"10.24963/ijcai.2024/146","url":"https://ijcai.org/proceedings/2024/146","pdf":"https://ijcai.org/proceedings/2024/0146.pdf","markdown":"https://ijcai.org/proceedings/2024/146.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/146","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":146,"pages":{"first":1317,"last":1325},"keywords":["Computer Vision: CV: Applications","Computer Vision: CV: Multimodal learning"],"videos":[],"references":[{"position":1,"raw":"[Afifi et al., 2021] Mahmoud Afifi, Konstantinos G Derpa- nis, Bjorn Ommer, and Michael S Brown. 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