Snapshot spectral imaging via compressive random convolution

Spectral imaging is of interest in many applications, including wide-area airborne surveillance, remote sensing, and tissue spectroscopy. Coded aperture spectral snapshot imaging (CASSI) provides an efficient mechanism to capture a 3D spectral cube with a single shot 2D measurement. CASSI uses a focal plane array (FPA) measurement of a spectrally dispersed, aperture coded, source. The spectral cube is then attained using a compressive sensing reconstruction algorithm. In this paper, we explore a new approach referred to as random convolution snapshot spectral imaging (RCSSI). It is based on FPA measurements of spectrally dispersed coherent sources that have been randomly convoluted by a spatial light modulator. The new method, based on the theory of compressive sensing via random convolutions, is shown to outperform traditional CASSI systems in terms of PSNR spectral image cube reconstructions.