Reduction of data acquisition time in Raman spectroscopy imaging using structure based compressive sampling algorithm

The Bayesian approach that utilizes the sparsity constraint and a priori statistical information to obtain near optimal estimates is presented. In addition, the wealthy structure of the sensing matrix including modularity, orthogonality and order recursive calculations is used to develop a fast sparse recovery algorithm. The performance of this algorithm is quite close to Convex Relaxation and Fast Bayesian Matching Pursuit algorithms at low sparsity rate while it outperforms Orthogonal Matching Pursuit algorithm by approximately 3 dB for the studied range of sparsity. The results show that the Structure based Compressive Sampling is a promising tool for obtaining Raman image reconstructions of quality in a reduced time of acquisition.