Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916874366746624 |
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| author | Bai, Haowen Zhang, Jiangshe Zhao, Zixiang Deng, Lilun Cui, Yukun Xu, Shuang |
| author_facet | Bai, Haowen Zhang, Jiangshe Zhao, Zixiang Deng, Lilun Cui, Yukun Xu, Shuang |
| contents | Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image illumination from scene reflectance, provides a natural framework to ensure consistent scene representation and effective information fusion across varied exposure levels. However, the conventional pixel-wise multiplication of illumination and reflectance inadequately models the glare effect induced by overexposure. To address this limitation, we introduce an unsupervised and controllable method termed Retinex-MEF. Specifically, our method decomposes multi-exposure images into separate illumination components with a shared reflectance component, and effectively models the glare induced by overexposure. The shared reflectance is learned via a bidirectional loss, which enables our approach to effectively mitigate the glare effect. Furthermore, we introduce a controllable exposure fusion criterion, enabling global exposure adjustments while preserving contrast, thus overcoming the constraints of a fixed exposure level. Extensive experiments on diverse datasets, including underexposure-overexposure fusion, exposure controlled fusion, and homogeneous extreme exposure fusion, demonstrate the effective decomposition and flexible fusion capability of our model. The code is available at https://github.com/HaowenBai/Retinex-MEF |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2503_07235 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion Bai, Haowen Zhang, Jiangshe Zhao, Zixiang Deng, Lilun Cui, Yukun Xu, Shuang Computer Vision and Pattern Recognition Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image illumination from scene reflectance, provides a natural framework to ensure consistent scene representation and effective information fusion across varied exposure levels. However, the conventional pixel-wise multiplication of illumination and reflectance inadequately models the glare effect induced by overexposure. To address this limitation, we introduce an unsupervised and controllable method termed Retinex-MEF. Specifically, our method decomposes multi-exposure images into separate illumination components with a shared reflectance component, and effectively models the glare induced by overexposure. The shared reflectance is learned via a bidirectional loss, which enables our approach to effectively mitigate the glare effect. Furthermore, we introduce a controllable exposure fusion criterion, enabling global exposure adjustments while preserving contrast, thus overcoming the constraints of a fixed exposure level. Extensive experiments on diverse datasets, including underexposure-overexposure fusion, exposure controlled fusion, and homogeneous extreme exposure fusion, demonstrate the effective decomposition and flexible fusion capability of our model. The code is available at https://github.com/HaowenBai/Retinex-MEF |
| title | Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.07235 |