The Marginal Importance of Known Distortions and Alignment in CASSI Systems

Coded Aperture Snapshot Spectral Imagers (CASSI) are designed to acquire 2D coded acquisitions of hyperspectral scenes (HSS). Associated with reconstruction algorithms, they allow to analyze the HSS using a small amount of data. Various configurations of such imagers exist, with different distortions. In this article we show that reconstruction quality is relatively insensitive to these distortions, if known and correctly modeled. To this end, we introduce a differentiable ray-tracing-based model that incorporates aberrations and distortions to render coded hyperspectral acquisitions using CASSI. Such simulated acquisitions are used to train state-of-the-art hyperspectral cube reconstruction algorithms. We also adapted these algorithms through the use of a ray-tracing-based mapping function which accounts for aberrations and distortions. We evaluated four comparable CASSI systems with varying degree of optical aberrations and misalignments, using five state-of-the-art hyperspectral cube reconstruction algorithms. Our analyses show that if known and properly modeled, the effects of geometric distortions of the system and misalignment of the dispersive elements have a marginal impact on the overall reconstruction quality. Therefore, relaxing traditional constraints on measurement conformity and fidelity to the scene enables the development of novel imaging instruments, guided by performance metrics applied to the design or the processing of acquisitions. By providing a complete framework for design, simulation and evaluation, this work contributes to the optimization and exploration of new CASSI systems and more generally to the computational imaging community.