Abstract
<p class="p1">The coded aperture snapshot spectral imaging system (CASSI) is an imaging architecture which senses the three dimensional informa-tion of a scene with two dimensional (2D) focal plane array (FPA) coded projection measurements. A reconstruction algorithm takes advantage of the compressive measurements sparsity to recover the underlying 3D data cube. Traditionally, CASSI uses block-un-block coded apertures (BCA) to spatially modulate the light. In CASSI the quality of the reconstructed images depends on the design of these coded apertures and the FPA dynamic range. This work presents a new CASSI architecture based on grayscaled coded apertu-res (GCA) which reduce the FPA saturation and increase the dynamic range of the reconstructed images. The set of GCA is calculated in a real-time adaptive manner exploiting the information from the FPA compressive measurements. Extensive simulations show the attained improvement in the quality of the reconstructed images when GCA are employed. In addition, a comparison between traditional coded apertures and GCA is realized with respect to noise tolerance.</p>
Highlights
The coded aperture snapshot spectral imaging system (CASSI) is an imaging architecture which senses the three dimensional information of a scene with a single two dimensional (2D) coded random projection measurement set (Wagadarikar, et al 2008)
The CASSI optical architecture comprises five optical elements: an objective lens is used to form an image of a scene in the plane of the coded aperture; a coded aperture modulates the spatial information over the complete wavelength range; a relay lens transmits the coded light field onto a dispersive element that disperses the light before it impinges on the focal plane array (FPA)
In order to improve intensity modulation, we propose the use of grayscale-adaptive coded apertures (GCA) which can be implemented using a DMD
Summary
The coded aperture snapshot spectral imaging system (CASSI) is an imaging architecture which senses the three dimensional information of a scene with a single two dimensional (2D) coded random projection measurement set (Wagadarikar, et al 2008). Given a set of compressive measurements, compressive sensing theory (CS) (Candes 2006, Donoho 2006, Baraniuk 2007) is used to reconstruct the underlying data cube of size N × N × L from just N (N + L − 1) measurements, where N is the spatial dimension and L is the spectral depth of the data cube. In. CASSI, the quality of the reconstructed images relies on the design of the set of 2D coded apertures, whose entries
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