从生物视觉启发，用灰像素检测实现更稳定的色彩恒常性
Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods
- 基于灰像素检测构建统一理论框架
- 实验验证灰像素法在多种光照下有效提升色彩一致性
- 适合对生物启发算法和图像色彩处理感兴趣的读者
色彩恒常性是许多生物视觉系统的基本能力，也是计算机成像中的关键步骤。生物启发建模为揭示色彩恒常性的计算原理并开发高效算法提供了新思路。然而，当前生物启发的色彩恒常性方法仍缺乏系统分析。本文提出一个整合生物学机制、计算理论与算法实现的综合性技术框架：首先系统回顾生物色彩恒常性的计算理论，表明光源估计可归结为早期视觉中灰锚点（像素或表面）的检测；随后在朗伯反射模型和生物色对立机制下，统一重释典型灰像素检测方法，如灰像素法与灰度指数法；最后提出一种简单学习方法，将反射模型约束与特征学习结合，探索基于灰像素检测的生物启发色彩恒常性潜力。大量实验证明灰像素检测在色彩恒常性中的有效性，并展示了生物启发方法的巨大潜力。
原文摘要 · Abstract (English)
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.
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