Strong recovery conditions for least support orthogonal matching pursuit in noisy case

Abstract

A least support denoising-orthogonal matching pursuit (LSD-OMP) algorithm to reconstruct the sparse signal using less number of iterations from noisy measurements is presented. The algorithm achieves correct support recovery without requiring sparsity knowledge. An improved restricted isometry property-based condition is derived over the best-known results. Experimental results demonstrate that the LSD-OMP achieves good performance on recovering sparse signals, outperforming the latest state-of-the art method in terms of reconstructed signal-to-noise ratio and running time.