Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods

TL;DR

This paper develops a comprehensive bio-inspired framework for color constancy, emphasizing gray-pixel detection and integrating biological, computational, and algorithmic insights, with promising experimental results.

Contribution

It revisits biological color constancy theory, unifies gray-pixel detection methods under a common framework, and introduces a new learning-based approach.

Findings

Gray-pixel detection effectively estimates illumination in color constancy.

Unified framework links Gray-Pixel and Grayness-Index methods with biological mechanisms.

Proposed learning-based method improves color constancy performance.

Abstract

Color constancy is a fundamental ability of many biological visual systems and a crucial step in computer imaging systems. Bio-inspired modeling offers a promising way to elucidate the computational principles underlying color constancy and to develop efficient computational methods. However, bio-inspired methods for color constancy remain underexplored and lack a comprehensive analysis. This paper presents a comprehensive technical framework that integrates biological mechanisms, computational theory, and algorithmic implementation for bio-inspired color constancy. Specifically, we systematically revisit the computational theory of biological color constancy, which shows that illuminant estimation can be reduced to the task of gray-anchor (pixel or surface) detection in early vision. Subsequently, typical gray-pixel detection methods, including Gray-Pixel and Grayness-Index, are reinterpreted within a unified theoretical framework with the Lambertian reflection model and biological color-opponent mechanisms. Finally, we propose a simple learning-based method that couples reflection-model constraints with feature learning to explore the potential of bio-inspired color constancy based on gray-pixel detection. Extensive experiments confirm the effectiveness of gray-pixel detection for color constancy and demonstrate the potential of bio-inspired methods.