Image reconstruction using modified orthogonal matching pursuit and compressive sensing

Compressive sensing system merges sampling and compression for a given sparse signal. It can reconstruct the image accurately by using fewer linear measurements than the original measurements. Hence, it is able to achieve reduction in complexity of sampling and number of computations. Since existing algorithms for implementation of sampling for the whole image are time consuming and it requires huge storage space, greedy approaches are used commonly to recover sparse signals from fewer measurements. One of the commonly used greedy approaches is Orthogonal Matching Pursuit (OMP), which can iteratively reconstruct the image. In this paper, modified form of OMP is presented in which stopping condition specified by the Recovery condition and Mutual incoherence property is used on the low frequency coefficients of the image. The simulation result using modified OMP shows that the reconstructed image achieves better PSNR and uses lesser number of measurements.