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Spectral imaging enables spatially-resolved identification of materials inremote sensing, biomedicine, and astronomy. However, acquisition times requirebalancing spectral and spatial resolution with signal-to-noise. Hyperspectralimaging provides superior material specificity, while multispectral images arefaster to collect at greater fidelity. We propose an approach for fusinghyperspectral and multispectral images to provide high-quality hyperspectraloutput. The proposed optimization leverages the least absolute shrinkage andselection operator (LASSO) to perform variable selection and regularization.Computational time is reduced by applying the alternating direction method ofmultipliers (ADMM), as well as initializing the fusion image by estimating itusing maximum a posteriori (MAP) based on Hardie's method. We demonstrate thatthe proposed sparse fusion and reconstruction provides quantitatively superiorresults when compared to existing methods on publicly available images.Finally, we show how the proposed method can be practically applied inbiomedical infrared spectroscopic microscopy.

Hyperspectral-Multispectral Image Fusion with Weighted LASSO