Compressed sensing for image reconstruction via back-off and rectification of greedy algorithm

Abstract

Abstract Image reconstruction is an important research topic in the field of multimedia processing. It aims to represent a high-resolution image with highly compressed features that can be used to reconstruct the original image as well as possible, and has been widely used for image storage and transmission. Compressed Sensing (CS) is a commonly used approach for image reconstruction; however, CS currently lacks an efficient and accurate solving algorithm. To this end, we present an iterative greedy reconstruction algorithm for Compressed Sensing called back-off and rectification of greedy pursuit (BRGP). The most significant feature of the BRGP algorithm is that it uses a back-off and rectification mechanism to select the atoms and then obtains the final support set. Specifically, an intersection of support sets estimated by the Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP) algorithms is first set as the initial candidate support, and then a back-off and rectification mechanism is used to expand and rectify it. Experimental results show that the algorithm significantly outperforms conventional techniques for one-dimensional or two-dimensional compressible signals.