Comparison of reconstruction algorithm based on different priors for snapshot compressive spectral imaging

Snapshot Compressive Spectral Imaging (SCPI) is a computational imaging technique that reconstructs three-dimensional (3D) spectral datacube from two-dimensional (2D) compressive measurements. The dual-disperser coded aperture snapshot spectral imaging (DD-CASSI) system is one of the prototypes to implement the SCPI technique. It can simultaneously acquire and compress the spectral images of target scene, and then the spectral images can be reconstructed from the compressive measurements. Some image priors such as Deep Image Prior (DIP), sparsity prior, low-rank prior and Total Variation (TV) prior can be used to improve the performance of different SCPI reconstruction algorithms. In this paper, we compare the spectral image reconstruction approaches based on the split Bregman algorithm combined with different image priors. These algorithms are assessed based on both simulation data and experimental testbed of DD-CASSI system. Simulation and experimental results show that the DIP prior can achieve better reconstruction performance compared to the other three image priors.