Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
Abstract
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.
Cite
@article{arxiv.2503.14774,
title = {Revisiting Image Fusion for Multi-Illuminant White-Balance Correction},
author = {David Serrano-Lozano and Aditya Arora and Luis Herranz and Konstantinos G. Derpanis and Michael S. Brown and Javier Vazquez-Corral},
journal= {arXiv preprint arXiv:2503.14774},
year = {2025}
}
Comments
10 pages