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

arXiv cs.CV / 4/23/2026

📰 NewsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper argues that color constancy, inspired by biological vision, can be analyzed by framing illuminant estimation as a gray-anchor detection problem in early vision.
It revisits the computational theory behind biological color constancy and shows how gray-anchor (pixel or surface) detection underpins illuminant estimation.
Existing gray-pixel approaches such as Gray-Pixel and Grayness-Index are reinterpreted under a unified theory combining the Lambertian reflection model and biological color-opponent mechanisms.
The authors introduce a simple learning-based method that integrates reflection-model constraints with feature learning, leveraging gray-pixel detection to advance bio-inspired color constancy methods.
Experiments validate that gray-pixel detection is effective for color constancy and that bio-inspired approaches can achieve promising 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.