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

2026-04-22

AI Summary

This work addresses the problem of color constancy by proposing a unified framework that integrates biological visual mechanisms with computational modeling. Inspired by the “gray anchor” hypothesis in early vision, the method reformulates illuminant estimation as a gray pixel detection task, systematically unifying gray anchor theory with gray pixel approaches for the first time. The proposed model incorporates Lambertian reflectance, biological color-opponent mechanisms, and learned features within a constrained optimization framework. Experimental results demonstrate the effectiveness of the approach across multiple benchmark datasets, achieving significantly improved estimation accuracy and highlighting the potential and advantages of biologically inspired strategies in computational color constancy.

Technology Category

• Computer Vision: Medical and Biological Imaging
• Machine Learning: Calibration & Uncertainty Quantification
• Constraint Satisfaction and Optimization: Other Foundations of Constraint Satisfaction

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• color constancy
• bio-inspired
• gray pixel
• computational theory
• illuminant estimation

Innovation

• bio-inspired color constancy
• gray-anchor detection
• gray-pixel methods
• Lambertian reflection model
• color-opponent mechanisms