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Compressive spectral imaging has demonstrated to be a reliable way to capture multispectral data using far few measurements than traditional scanning techniques. These systems capture coded and multiplexed projections of the underlying scene, and then exploits the theory of compressive sensing to solve an optimization algorithm which attains an estimation of the 3D spatio-spectral data cube. Up to...
Coded aperture compressive spectral imagers allow capturing spectral imaging information of a 3D cube with just a single 2D measurement of the coded and spectrally dispersed source field. These imagers systems often rely on existing FPA detectors, SLMs, and micro mirror devices, which are often mismatched in pitch size and pixel resolution. A traditional solution consists on grouping several pixels...
A new code aperture design framework for multiframe code aperture snapshot spectral imaging (CASSI) system is presented. It aims at the optimization of code aperture sets such that a group of compressive spectral measurements is constructed, each with information from a specific subset of bands. A matrix representation of CASSI is introduced that permits the optimization of spectrally selective code...
The Restricted Isometry Property (RIP) is an important concept in compressed sensing. It is well known that many random matrices satisfy the RIP with high probability, whenever the entries of the random matrix have finite second order moment. Recent work in compressed sensing has shown that it is possible to do dimensionality reduction and signal reconstruction using Cauchy random projections. This...
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