Multispectral Demosaicking Using Bilinear Decomposition With Multichannel Structural Regularization
Sanghoon Kim, Jinook Lee, Moon Gi Kang · IEEE Access · 2026
In this paper, a multispectral demosaicking algorithm that builds upon bilinear decomposition of a color image is proposed. The overall structure of the proposed algorithm can be reduced into three parts, where the first part constructs a graph-based measure that confines the structure of neighboring pixels. The second part decomposes the raw observation into panchromatic image and chromaticity component, while the third part reconstructs the multispectral data using the decomposed component. Contrary to previous methods that identify the panchromatic image before chromaticity or spectral difference, the proposed method regularizes the mosaicked chromaticity with bilateral weights that operate as an interpolation stencil to promote similarity between the two, thereby suppressing pattern artifacts. Then, the weights formerly utilized in the identification of the mosaicked chromaticity are applied to interpolate and refine the two components, thus reconstructing chromaticity that has concurrent edges with the panchromatic image. The proposed method demonstrated results comparable to and superior to those of other state-of-the-art algorithms in both quantitative and qualitative evaluations, showing less aliasing and pattern artifacts with an accurate visual representation of the trichromatic rendition for the reconstructed multispectral data.