Enhancement of CASSI by a zero-order image employing a single detector

Differential coded aperture single-snapshot spectral imaging

We propose a novel, to the best of our knowledge, concept of the differential coded aperture snapshot spectral imaging (D-CASSI) technique exploiting the benefits of using a {-1,+1} random mask, which is demonstrated by a broadband single-snapshot hyperspectral camera using compressed sensing. To double the information, we encode the image with two complementary random masks, which proved to be superior to two independent patterns. We utilize dispersed and non-dispersed encoded images captured in parallel on a single detector. We explored several different approaches to processing the measured data, which demonstrates significant improvement in retrieving complex hyperspectral scenes. The experiments were completed by simulations in order to quantify the reconstruction fidelity. The concept of differential CASSI could be easily implemented also by multi-snapshot CASSI without any need for optical system modification.

Single-shot compressive hyperspectral imaging with dispersed and undispersed light using a generally available grating

Commercially available hyperspectral cameras are useful for remote sensing, but in most cases snapshot imaging is difficult due to the need for scanning. The coded aperture snapshot spectral imager (CASSI) has been proposed to simultaneously acquire a target scene's spatial and spectral dimensional data, employing a refractive prism as a disperser. This paper proposes a CASSI-based technique using a generally available diffraction grating of a Ronchi ruling and blazed grating and its improvement using the undispersed zeroth-order light. The feasibility and performance of the proposed technique are experimentally validated, and the grating parameters are identified.

Coded Aperture Hyperspectral Image Reconstruction

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.