Coded Aperture Hyperspectral Image Reconstruction

Abstract

In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.

Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System

“…It is worth recalling that the analysis done in this study has been carried out considering the particular compressive hyperspectral camera DD-CASSI as a representative example of the CASSI group and the image reconstruction technique based on CGNE, which is a widely spread algorithm for solving inverse problems. Nevertheless, the general conclusions drawn in this paper are meant to still be valid (in terms of orders of magnitude for the performance and main trends) for other compressive devices (as they can be modeled by linear operators with sparse transfer matrices [14]) and other image reconstruction algorithms based on iterative variational approaches.…”

“…Indeed, the raw data acquired by the CASSI cannot be exploited as is, and an inverse problem has to be solved. A large set of algorithms exist to address image reconstruction and recover the data cube, including the Gradient Descent, Orthogonal Matching Pursuit (OMP), Gradient Projection for Sparse Reconstruction (GPSR), Least Absolute Shrinkage and Selection Operator (LASSO) and Iterative Shrinkage/Tresholding (IST); see [14] for a review.…”