Enhancing multi-illuminant color constancy through multi-scale estimation and high-frequency preservation
摘要
Color constancy is a crucial visual ability that allows humans to perceive consistent colors under varying illuminations. However, cameras often struggle with this under multi-illuminant scenes, resulting in local color casts in images. This paper addresses the challenge of multi-illuminant color constancy, aiming to estimate an illuminant map from an image. Existing methods rely either on the unverified assumption of smooth distribution, or on additional hand-tuned parameters, and prior knowledge about the illuminant, constraining their practical application. Through frequency analysis, we reveal that the illuminant map can contain high-frequency components, violating the smoothness assumption, and as its scale decreases, its frequency distribution shifts toward low-frequency regions. Inspired by these findings, we propose a network comprising both a low-frequency branch and a high-frequency branch. The two branches use U-Nets to estimate low- and high-frequency illuminant maps from small- and large-scale images, respectively. To preserve high-frequency features within the high-frequency branch, we embed a high-frequency preservation gating module (HFPGM) within the skip connections to filter and retain high-frequency features. Furthermore, we embed a cross-scale high-frequency preservation gating module (CSHFPGM) between the two branches. This module leverages the feature discrepancy between the decoding paths of both branches as a constraint and filters the features output by the high-frequency branch decoder. Our method achieves mean angular errors of