Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces

TL;DR

This paper investigates how reducing spectral dimensions in hyperspectral imaging affects illuminant estimation, demonstrating that compact spectral representations can outperform traditional RGB methods under certain conditions.

Contribution

It systematically analyzes the impact of spectral dimensionality reduction on hyperspectral illuminant estimation using the CbC framework, providing practical insights.

Findings

Spectral dimensionality reduction can improve illuminant estimation accuracy.

Compact spectral representations outperform RGB-based approaches in specific scenarios.

The study offers guidelines for efficient hyperspectral data utilization.

Abstract

Illuminant estimation aims to infer scene illumination from image measurements despite intrinsic ambiguities between surface reflectance and lighting. Most existing methods operate on trichromatic RGB images and are therefore fundamentally limited by the restricted spectral information available. Hyperspectral imaging provides a much richer representation of scene radiance and has the potential to alleviate these ambiguities. However, its high dimensionality poses computational and statistical challenges. In this work, we systematically study the effect of spectral dimensionality and representation choice on illuminant estimation performance using hyperspectral data. We adopt the practical and effective Color-by-Correlation (CbC) framework as the estimation backbone and analyze its behavior under different spectral dimensionality reduction strategies. Our results offer practical insights into how hyperspectral information can be efficiently exploited for illuminant estimation and identify conditions under which compact spectral representations outperform conventional RGB-based approaches. The code is available at https://github.com/IVRL/Reduced-Spectral-Color-Constancy.