{"type":"ScholarlyArticle","title":"TextMEF: Text-guided Prompt Learning for Multi-exposure Image Fusion","authors":[{"name":"Jinyuan Liu","slug":"jinyuan-liu","given":"Jinyuan","family":"Liu","affiliation":""},{"name":"Qianjun Huang","slug":"qianjun-huang","given":"Qianjun","family":"Huang","affiliation":""},{"name":"Guanyao Wu","slug":"guanyao-wu","given":"Guanyao","family":"Wu","affiliation":""},{"name":"Di Wang","slug":"di-wang","given":"Di","family":"Wang","affiliation":""},{"name":"Zhiying Jiang","slug":"zhiying-jiang","given":"Zhiying","family":"Jiang","affiliation":""},{"name":"Long Ma","slug":"long-ma","given":"Long","family":"Ma","affiliation":""},{"name":"Risheng Liu","slug":"risheng-liu","given":"Risheng","family":"Liu","affiliation":""},{"name":"Xin Fan","slug":"xin-fan","given":"Xin","family":"Fan","affiliation":""}],"abstract":"Multi-exposure image fusion~(MEF) aims to integrate a set of low dynamic range images, producing a single image with a higher dynamic range than either one. Despite significant advancements, current MEF approaches still struggle to handle extremely over- or under-exposed conditions, resulting in unsatisfactory visual effects such as hallucinated details and distorted color tones. With this regard, we propose TextMEF, a prompt-driven fusion method enhanced by prompt learning, for multi-exposure image fusion. Specifically, we learn a set of prompts based on text-image similarity among negative and positive samples (over-exposed, under-exposed images, and well-exposed ones). These learned prompts are seamlessly integrated into the loss function, providing high-level guidance for constraining non-uniform exposure regions. Furthermore, we develop a attention Mamba module effectively translates over-/under- exposed regional features into exposure invariant space and ensure them to build efficient long-range dependency to high dynamic range image. Extensive experimental results on three publicly available benchmarks demonstrate that our TextMEF significantly outperforms state-of-the-art approaches in both visual inspection and objective analysis.","doi":"10.24963/ijcai.2025/175","url":"https://ijcai.org/proceedings/2025/175","pdf":"https://ijcai.org/proceedings/2025/0175.pdf","markdown":"https://ijcai.org/proceedings/2025/175.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/175","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":175,"pages":{"first":1567,"last":1575},"keywords":["Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}