Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation and Fusion
Journal:
arXiv
Published Date:
Feb 4, 2025
Abstract
Multi-illuminant color constancy methods aim to eliminate local color casts
within an image through pixel-wise illuminant estimation. Existing methods
mainly employ deep learning to establish a direct mapping between an image and
its illumination map, which neglects the impact of image scales. To alleviate
this problem, we represent an illuminant map as the linear combination of
components estimated from multi-scale images. Furthermore, we propose a
tri-branch convolution networks to estimate multi-grained illuminant
distribution maps from multi-scale images. These multi-grained illuminant maps
are merged adaptively with an attentional illuminant fusion module. Through
comprehensive experimental analysis and evaluation, the results demonstrate the
effectiveness of our method, and it has achieved state-of-the-art performance.
