Optimized Coded Aperture Design in Compressive Spectral Imaging via Coherence Minimization

The coded aperture snapshot spectral imager (CASSI) system senses spatial and spectral information using a binary coded aperture and a dispersive element, thus the quality of reconstructed hyperspectral images is mainly determined by the structure of coded apertures. Traditional coded apertures (Random, Bernoulli, etc.), encoding hyperspectral images in focal array plane, suffer from suboptimal reconstruction accuracy. Therefore, optimizing coded aperture design improves the reconstruction quality for the scene. In this paper, a fast iterative algorithm performing coded apertures is investigated. Since calculating restricted isometry constant is NP-hard, the structure of coded apertures is alternatively designed based on mutual coherence property, via CASSI sensing matrice improvement. It has been proven that by exploiting blue noise spatial-spectral characteristics, the clusters of one-valued (white) entries in the coded aperture ensembles can be reduced. Finally, simulations are carried out on a real data set, showing the superiority of the proposed coded apertures in terms of various evaluation metrics.