用降维光谱数据提升高光谱成像下的颜色恒常性估计
Color Constancy in Hyperspectral Imaging via Reduced Spectral Spaces
- 基于相关性的颜色估计算法结合光谱降维策略
- 低维光谱表示在光照估计上优于传统RGB方法
- 为高光谱数据高效利用提供实证指导
光源估计旨在从图像测量中推断场景光照，尽管表面反射率与照明之间存在固有歧义。现有大多数方法基于三色RGB图像，受限于有限的光谱信息。高光谱成像提供了更丰富的场景辐射表示，有望缓解此类歧义。然而其高维度带来计算与统计挑战。本文系统研究了光谱维度与表示选择对光源估计性能的影响，采用实用有效的颜色-相关性（CbC）框架作为估计基础，分析不同光谱降维策略下的表现。结果揭示了如何高效利用高光谱信息进行光源估计，并识别出紧凑光谱表示优于传统RGB方法的条件。代码已开源。
原文摘要 · Abstract (English)
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.
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