No abstract

Compressive spectral imaging (CSI) senses the spatio-spectral information of a scene by measuring 2D coded projections on a focal plane array. A ℓ1-norm-based optimization algorithm is then used to recover the underlying discretized spectral image. The coded aperture snapshot spectral imager (CASSI) is an architecture realizing CSI where the reconstruction image quality relies on the design of a 2D set of binary coded apertures which block-unblock the light from the scene. This paper extends the compressive capabilities of CASSI by replacing the traditional blocking-unblocking coded apertures by a set of colored coded apertures. The colored coded apertures are optimized such that the number of projections is minimized while the quality of reconstruction is maximized. The optimal design of the colored coded apertures aims to better satisfy the restricted isometry property in CASSI. The optimal designs are compared with random colored coded aperture patterns and with the traditional blocking-unblocking coded apertures. Extensive simulations show the improvement in reconstruction PSNR attained by the optimal colored coded apertures designs.

Coded aperture spectral imaging (CASSI) provides a mechanism to capture a 3D spectral cube with a single shot 2D measurement. This paper extends the concept of CASSI to a system admitting multiple shot measurements which leads not only to higher quality of reconstruction, but also to spectrally selective imaging when the sequence of code aperture patterns is optimized. The aperture code optimization problem is shown to be analogous to the optimization of a constrained, multichannel filter bank. The optimal code apertures allow the decomposition of the CASSI measurements into several matrices, each having compressive information from only a few selected spectral bands. Each matrix is reconstructed separately and the results are merged if the full data cube is needed. This technique is equivalent to a filter bank decomposition of the CASSI measurements. The approach shows better quality and higher speed of reconstruction than a non-optimized multishot CASSI system. A number of simulations are developed to illustrate the spectral imaging characteristics attained by optimal aperture codes.

Coded aperture snapshot spectral imaging systems (CASSI) sense the three-dimensional spatio-spectral information of a scene using a single two-dimensional focal plane array snapshot. The compressive CASSI measurements are often modeled as the summation of coded and shifted versions of the spectral voxels of the underlying scene. This coarse approximation of the analog CASSI sensing phenomena is then compensated by calibration preprocessing prior to signal reconstruction. This paper develops a higher-order precision model for the optical sensing in CASSI that includes a more accurate discretization of the underlying signals, leading to image reconstructions less dependent on calibration. Further, the higher-order model results in improved image quality reconstruction of the underlying scene than that achieved by the traditional model. The proposed higher precision computational model is also more suitable for reconfigurable multiframe CASSI systems where multiple coded apertures are used sequentially to capture the hyperspectral scene. Several simulations and experimental measurements demonstrate the benefits of the discretization model.

A new code aperture design framework for multiframe code aperture snapshot spectral imaging (CASSI) system is presented. It aims at the optimization of code aperture sets such that a group of compressive spectral measurements is constructed, each with information from a specific subset of bands. A matrix representation of CASSI is introduced that permits the optimization of spectrally selective code aperture sets. Furthermore, each code aperture set forms a matrix such that rank minimization is used to reduce the number of CASSI shots needed. Conditions for the code apertures are identified such that a restricted isometry property in the CASSI compressive measurements is satisfied with higher probability. Simulations show higher quality of spectral image reconstruction than that attained by systems using Hadamard or random code aperture sets.