Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866917960978792448 |
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| author | Serrano-Lozano, David Arora, Aditya Herranz, Luis Derpanis, Konstantinos G. Brown, Michael S. Vazquez-Corral, Javier |
| author_facet | Serrano-Lozano, David Arora, Aditya Herranz, Luis Derpanis, Konstantinos G. Brown, Michael S. Vazquez-Corral, Javier |
| contents | White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal for common multi-illuminant scenarios. Additionally, existing fusion-based methods rely on sRGB WB datasets lacking dedicated multi-illuminant images, limiting both training and evaluation. To address these challenges, we introduce two key contributions. First, we propose an efficient transformer-based model that effectively captures spatial dependencies across sRGB WB presets, substantially improving upon linear fusion techniques. Second, we introduce a large-scale multi-illuminant dataset comprising over 16,000 sRGB images rendered with five different WB settings, along with WB-corrected images. Our method achieves up to 100\% improvement over existing techniques on our new multi-illuminant image fusion dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_14774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Revisiting Image Fusion for Multi-Illuminant White-Balance Correction Serrano-Lozano, David Arora, Aditya Herranz, Luis Derpanis, Konstantinos G. Brown, Michael S. Vazquez-Corral, Javier Computer Vision and Pattern Recognition White balance (WB) correction in scenes with multiple illuminants remains a persistent challenge in computer vision. Recent methods explored fusion-based approaches, where a neural network linearly blends multiple sRGB versions of an input image, each processed with predefined WB presets. However, we demonstrate that these methods are suboptimal for common multi-illuminant scenarios. Additionally, existing fusion-based methods rely on sRGB WB datasets lacking dedicated multi-illuminant images, limiting both training and evaluation. To address these challenges, we introduce two key contributions. First, we propose an efficient transformer-based model that effectively captures spatial dependencies across sRGB WB presets, substantially improving upon linear fusion techniques. Second, we introduce a large-scale multi-illuminant dataset comprising over 16,000 sRGB images rendered with five different WB settings, along with WB-corrected images. Our method achieves up to 100\% improvement over existing techniques on our new multi-illuminant image fusion dataset. |
| title | Revisiting Image Fusion for Multi-Illuminant White-Balance Correction |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.14774 |