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Coded aperture snapshot spectral imaging (CASSI) is a technique used toreconstruct three-dimensional hyperspectral images (HSIs) from one or severaltwo-dimensional projection measurements. However, fewer projection measurementsor more spectral channels leads to a severly ill-posed problem, in which caseregularization methods have to be applied. In order to significantly improvethe accuracy of reconstruction, this paper proposes a fast alternatingminimization algorithm based on the sparsity and deep image priors (Fama-SDIP)of natural images. By integrating deep image prior (DIP) into the principle ofcompressive sensing (CS) reconstruction, the proposed algorithm can achievestate-of-the-art results without any training dataset. Extensive experimentsshow that Fama-SDIP method significantly outperforms prevailing leading methodson simulation and real HSI datasets.

A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors