Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces

2026-05-13

AI Summary

This work addresses the inherent ambiguity in illumination estimation caused by the entanglement of surface reflectance and lighting, a challenge exacerbated by the limited spectral information in conventional RGB images. Leveraging hyperspectral imaging data, the study systematically investigates how spectral dimensionality and its representation influence illumination estimation performance. Building upon the Color-by-Correlation framework and integrating multiple spectral dimensionality reduction strategies, the authors demonstrate that compact spectral representations can outperform traditional RGB-based methods under specific conditions. The results validate that appropriately reduced hyperspectral data significantly enhances estimation accuracy across multiple benchmarks, offering a practical and efficient pathway for hyperspectral color constancy.

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.

Problem

• Color Constancy
• Illuminant Estimation
• Hyperspectral Imaging
• Spectral Dimensionality Reduction

Innovation

• Hyperspectral Imaging
• Color Constancy
• Illuminant Estimation
• Spectral Dimensionality Reduction
• Color-by-Correlation