Illumination Spectrum Estimation for Multispectral Images Using Illuminant Prior

Multispectral (MS) imaging, which captures richer spectral information than traditional three-channel RGB images, enables more precise reconstruction of illuminant spectral power distribution. However, accurate illuminant spectrum estimation (ISE) using MS images remains a challenging task, as existing studies often neglect the physical characteristics of spectral images. To address these challenges, we propose a novel deep learning model that incorporates illuminant prior (IP) information, extending a Gray-World assumption commonly used in RGB color constancy. In particular, the IP enhances the accuracy of the proposed network through an IP-aware attention network. We demonstrate the superiority of our proposed method through quantitative and qualitative results across various datasets for illumination spectral estimation in multispectral images.