Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods
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| Format: | Preprint |
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2026
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| _version_ | 1866914498276753408 |
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| author | Yang, Kai-Fu Luo, Fu-Ya Li, Yong-Jie |
| author_facet | Yang, Kai-Fu Luo, Fu-Ya Li, Yong-Jie |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_20243 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods Yang, Kai-Fu Luo, Fu-Ya Li, Yong-Jie Computer Vision and Pattern Recognition 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. |
| title | Bio-inspired Color Constancy: From Gray Anchoring Theory to Gray Pixel Methods |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.20243 |