Large Size of Color Constancy: Enhancing Pure Color Image Illuminant Estimation with Kolmogorov-Arnold Networks

Large efforts have been made to perform illuminant estimation, resulting in the development of various statistical- and learning-based methods. However, there have been challenges for some types of images, such as a single color, referred to as pure color images, which is the focus of the present research.. In this study, the neural network approach is used. It was found the Kolmogorov-Arnold Networks (KAN) model, a novel approach that diverges from traditional Multi-Layer Perceptron (MLP) architectures gave the accurate predictions. Our method,”Large Size Colour Constancy” (LSCC), characterized by its unique neural network structure, achieves high accuracy in illuminant estimation with significantly fewer parameters and enhanced interpretability. Additionally, three new pure color image datasets—”ZJU Color Fabric”,”ZJU 0.8 Real Scene”, and”ZJU 1.0 Real Scene” were produced—covering a wide range of conditions, including indoor and outdoor environments, as well as natural and artificial light sources. The results showed LSCC method to outperform existing methods across not only the pure colour datasets but also the traditional datasets, including classical normal images. It should offers practical deployment potential due to its efficiency and reduced computational requirements.