Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
Authors: name: David Serrano-Lozano, name: Aditya Arora, name: Luis Herranz, name: Konstantinos G. Derpanis, name: Michael S. Brown, name: Javier Vazquez-Corral

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.