High precision discretization model for coded aperture-based compressive spectral imaging

Coded aperture snapshot spectral imaging systems (CASSI) measure the 3D spatio-spectral information of a scene using several compressive 2D focal plane array (FPA) snapshots. The image reconstruction algorithms utilized in CASSI use a first-order approximation of the underlying analog sensing phenomena. A calibration method is then used to compensate for the coarse approximation - an approach not well suited for multishot CASSI systems. This paper develops a more accurate computational model for CASSI which provides a higher quality of image reconstruction. Several simulations are shown to illustrate the performance improvement attained by the new model.