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