Multispectral (MS) images contain richer spectral information than RGB images due to their increased number of channels and are widely used for various applications. However, achieving accurate estimation in MS images remains challenging, as previous studies have struggled with spectral diversity and the inherent entanglement between the illuminant and surface reflectance spectra. To tackle these challenges, in this paper, we propose a novel Illumination spectrum estimation technique for MS images via Surface reflectance modeling and Spatial-spectral feature generation (ISS). The proposed technique employs a learnable spectral unmixing (SU) block to enhance surface reflectance modeling, which was unattempted in the illumination spectrum estimation, and a feature mixing block to fuse spectral and spatial features of MS images with cross-attention. The features are refined iteratively and processed through a decoder to produce an illumination spectrum estimator. Experimental results demonstrate that the proposed technique achieves state-of-the-art performance in illumination spectrum estimation in various MS image datasets. The code is available at https://github.com/heyjinnii/ISS-MSI.git.

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.