# Investigating Color Illusions from the Perspective of Computational Color Constancy

arXiv: https://arxiv.org/abs/2312.13114 · HTML: https://mlsanity.com/paper/2312.13114/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 65 references that are in the map.
- First posted on arXiv 20 Dec 2023.

## Builds on (most cited first, 11 of 11)

- [Deep White-Balance Editing](https://mlsanity.com/paper/2004.01354/) (2020)
- [Cross-Camera Convolutional Color Constancy](https://mlsanity.com/paper/2011.11890/) (2020)
- [On Finding Gray Pixels](https://mlsanity.com/paper/1901.03198/) (2019)
- [Auto White-Balance Correction for Mixed-Illuminant Scenes](https://mlsanity.com/paper/2109.08750/) (2021)
- [Sensor-Independent Illumination Estimation for DNN Models](https://mlsanity.com/paper/1912.06888/) (2019)
- [Single and Multiple Illuminant Estimation Using Convolutional Neural Networks](https://mlsanity.com/paper/1508.00998/) (2015)
- [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)
- [Generative Models for Multi-Illumination Color Constancy](https://mlsanity.com/paper/2109.00863/) (2021)
- [Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity](https://mlsanity.com/paper/1609.01670/) (2016)

## Built on by (most cited first, 1 of 1)

- [The Art of Deception: Color Visual Illusions and Diffusion Models](https://mlsanity.com/paper/2412.10122/) (2024)

## Abstract (arXiv)

Color constancy and color illusion perception are two phenomena occurring in the human visual system, which can help us reveal unknown mechanisms of human perception. For decades computer vision scientists have developed numerous color constancy methods, which estimate the reflectance of the surface by discounting the illuminant. However, color illusions have not been analyzed in detail in the field of computational color constancy, which we find surprising since the relationship they share is significant and may let us design more robust systems. We argue that any model that can reproduce our sensation on color illusions should also be able to provide pixel-wise estimates of the light source. In other words, we suggest that the analysis of color illusions helps us to improve the performance of the existing global color constancy methods, and enable them to provide pixel-wise estimates for scenes illuminated by multiple light sources. In this study, we share the outcomes of our investigation in which we take several color constancy methods and modify them to reproduce the behavior of the human visual system on color illusions. Also, we show that parameters purely extracted from illusions are able to improve the performance of color constancy methods. A noteworthy outcome is that our strategy based on the investigation of color illusions outperforms the state-of-the-art methods that are specifically designed to transform global color constancy algorithms into multi-illuminant algorithms.