Covariance Matrix Estimation from Multiple Subsets in Compressive Spectral Imaging

Elkin Díaz,Jonathan Monsalve,Andrés Guerrero,Henry Arguello

Imaging and Applied Optics 2018 (3D, AO, AIO, COSI, DH, IS, LACSEA, LS&ampC, MATH, pcAOP)（2018）

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Abstract

This paper introduces an optimization problem to estimate the covariance matrix from multiple subsets of compressive measurements using random projection matrices. The proposed optimization is tested with computational simulations for the DD-CASSI and SSCSI optical architectures.

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