Multispectral (MS) imaging extends beyond conventional RGB imaging by capturing more spectral bands, thereby improving illuminant spectrum estimation (ISE). However, existing methods often fail to fully exploit spectral information, resulting in suboptimal performance under diverse lighting conditions and across different sensor domains. Hence, we propose a deep learning framework with a spatio-spectral feature extraction block, which incorporates spectral attention mechanisms to enhance spectral correlation and preserve illuminant-relevant spatial features. Through the inclusion of an illuminant prior (IP), our approach prioritizes specific channels that provide more meaningful information in an MS image. We also propose a spectral-domain transform across different MS sensor spaces. The results demonstrate that illuminant spectra learned in high-dimensional sensor spaces can be effectively transformed to various lower-dimensional camera sensor spaces without any additional training. To facilitate evaluation, we introduce a real-world MS dataset containing high-dimensional ground-truth illumination spectra captured under diverse lighting conditions. Through extensive experiments, we demonstrate that our method achieves superior accuracy compared to existing models, thus providing a practical solution for real-world ISE. The code and dataset are available at https://github.com/hyejin5/Spectrum-Aware-Illumination-Estimation-Using-Multispectral-Image.

翻译：多光谱成像通过捕获比传统RGB成像更多的光谱波段，从而提升了光源光谱估计的性能。然而，现有方法往往未能充分利用光谱信息，导致在不同光照条件和不同传感器域下性能欠佳。为此，我们提出了一种包含空谱特征提取模块的深度学习框架，该模块引入光谱注意力机制以增强光谱相关性并保留与光源相关的空间特征。通过引入光源先验，我们的方法能够优先关注多光谱图像中提供更有意义信息的特定通道。同时，我们还提出了跨不同多光谱传感器空间的光谱域变换方法。结果表明，在高维传感器空间中学习到的光源光谱可以有效转换到各种低维相机传感器空间，而无需额外训练。为便于评估，我们引入了一个包含高维真实光照光谱的真实多光谱数据集，该数据集在不同光照条件下采集。通过大量实验，我们证明了该方法相比现有模型具有更优的准确性，从而为真实场景中的光源光谱估计提供了实用解决方案。代码和数据集已开源至https://github.com/hyejin5/Spectrum-Aware-Illumination-Estimation-Using-Multispectral-Image。