A Compressive System Matrix Design in Spectral Imaging by a Homogenization Algorithm - Noriega, Camilo; Mejía, Yuri; Arguello, Henry (Ingeniería, 2016)
Context: Compressive spectral imaging systems (CSI) use a focal plane array (FPA) to measure two-dimensional (2D) coded projections of a three-dimensional (3D) spatiospectral scene. A reconstruction algorithm based on compressive sensing theory exploits the projections to retrieve the underlying 3D scene. Compressive sensing relies on two principles: Sparsity and incoherence. Higher incoherence drives to better-reconstructed image quality. In CSI systems, a random design of the coded apertures elements guarantees a high incoherence between the sensing matrix and the representation basis. However, when a coded aperture is designed completely random, it is possible that some voxels not be sensed at all or they be sensed more than once. Method: This paper presents a random algorithm for a colored coded apertures design by homogenizing defined parameters of the colored coded aperture snapshot spectral imaging system (C-CASSI) representative matrix. Homogenization parameters guarantee that all voxels are sensed at least once. The homogenization is achieved by weighting the selected parameters of the matrix, in this case, the average of unblocking elements per column and the average of unblocking elements per row. Results/Conclusions: Simulations show a higher performance in the PSNR of the reconstructed images by using the proposed approach, as compared to traditional random coded apertures.