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

arXiv: https://arxiv.org/abs/2604.20243 · HTML: https://mlsanity.com/paper/2604.20243/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 92 references that are in the map.
- First posted on arXiv 22 Apr 2026.

## Builds on (most cited first, 17 of 17)

- [Fast Fourier Color Constancy](https://mlsanity.com/paper/1611.07596/) (2016)
- [Convolutional Color Constancy](https://mlsanity.com/paper/1507.00410/) (2015)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [CLCC: Contrastive Learning for Color Constancy](https://mlsanity.com/paper/2106.04989/) (2021)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Cascading Convolutional Color Constancy](https://mlsanity.com/paper/1912.11180/) (2019)
- [Color Constancy Using CNNs](https://mlsanity.com/paper/1504.04548/) (2015)
- [Learning to Adapt to Light](https://mlsanity.com/paper/2202.08098/) (2022)
- [Unsupervised Learning for Color Constancy](https://mlsanity.com/paper/1712.00436/) (2017)
- [Single and Multiple Illuminant Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/1508.00998/) (2015)
- [Bag of Color Features For Color Constancy](https://mlsanity.com/paper/1906.04445/) (2019)
- [INTEL-TAU: A Color Constancy Dataset](https://mlsanity.com/paper/1910.10404/) (2019)
- [Rehabilitating the ColorChecker Dataset for Illuminant Estimation](https://mlsanity.com/paper/1805.12262/) (2018)
- [Color Constancy Convolutional Autoencoder](https://mlsanity.com/paper/1906.01340/) (2019)
- [Integral Fast Fourier Color Constancy](https://mlsanity.com/paper/2502.03494/) (2025)
- [Deep Structured-Output Regression Learning for Computational Color Constancy](https://mlsanity.com/paper/1607.03856/) (2016)
- [Gray Anchoring: a New Computational Theory for Biological Color Constancy](https://mlsanity.com/paper/2410.08823/) (2024)

## Abstract (arXiv)

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.