Coded Aperture Optimization in Spatial Spectral Compressive Spectral Imagers

Abstract

The spatial spectral compressive spectral imager (SSCSI) is a recently proposed optical architecture, where the spatial-spectral coding of a scene is jointly realized by a binary ON-OFF mask and a dispersive element. The quality of the recovered hyperspectral scene in SSCSI depends on various parameters, such as the coded aperture location with respect to the sensor, the coded aperture and detector pitch sizes, and the diffraction introduced by the grating. Given these parameters, the sensing matrix of the imager is largely dependent on the coded aperture structure and characteristics. As such, its optimization is critical to maximizing the quality of the recovered scene. In this article, an algorithm that optimizes the coded aperture patterns for SSCSI is derived. The final structure of such patterns turns out to be directly linked to the coded aperture position measured from the sensor. It is also shown that the optimal coded apertures in SSCSI adhere to green-blue noise spectral characteristics, where the degree of green spectral components depends on the location of the coded aperture with respect to the sensor. Simulations under different conditions are presented to demonstrate the underlying concepts.