Auto White-Balance Correction for Mixed-Illuminant Scenes

TL;DR

This paper introduces a novel auto white-balance correction method for scenes with mixed lighting, which does not require explicit illuminant estimation and uses learned blending of predefined white-balance renderings.

Contribution

The proposed method uniquely avoids illuminant estimation by learning to blend multiple white-balance renderings, improving correction in mixed-illuminant scenes.

Findings

Produces promising results compared to existing methods

Effective in both single- and mixed-illuminant scenes

Source code and models are publicly available

Abstract

Auto white balance (AWB) is applied by camera hardware at capture time to remove the color cast caused by the scene illumination. The vast majority of white-balance algorithms assume a single light source illuminates the scene; however, real scenes often have mixed lighting conditions. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. A unique departure from conventional AWB, our method does not require illuminant estimation, as is the case in traditional camera AWB modules. Instead, our method proposes to render the captured scene with a small set of predefined white-balance settings. Given this set of rendered images, our method learns to estimate weighting maps that are used to blend the rendered images to generate the final corrected image. Through extensive experiments, we show this proposed method produces promising results compared to other alternatives for single- and mixed-illuminant scene color correction. Our source code and trained models are available at https://github.com/mahmoudnafifi/mixedillWB.