Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
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.
67e2c6540a17d66e31b9fa9c
David Serrano-Lozano
claimed_verified
2025-03-25T19:02:12.938Z
6479b418bfaa9e96b850959b
https://cdn-avatars.huggingface.co/v1/production/uploads/6479b418bfaa9e96b850959b/qQSSgCK2M8ATutL6H0Yvv.jpeg
David Serrano Lozano
user
davidserra9
67e2c6540a17d66e31b9fa9d
Aditya Arora
67e2c6540a17d66e31b9fa9e
Luis Herranz
67e2c6540a17d66e31b9fa9f
Konstantinos G. Derpanis
67e2c6540a17d66e31b9faa0
Michael S. Brown
67e2c6540a17d66e31b9faa1
Javier Vazquez-Corral
Revisiting Image Fusion for Multi-Illuminant White-Balance Correction
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.
A transformer-based model for multi-illuminant white balance correction outperforms linear fusion methods using a new large-scale multi-illuminant dataset.
transformer-based model spatial dependencies sRGB WB presets multi-illuminant dataset WB-corrected images image fusion