Transforming the SMV model into MMV model based on the characteristics of wavelet coefficients

Abstract

Sparse signal recovery or compressed sensing (CS) theory has recently received attention in the image compression field. CS has proven that the successful recovery rate of the multiple measurement vectors (MMVs) model is higher than that of the single measurement vector (SMV) case. Most existing algorithms have focused on sparse signal recovery using the MMV model, without considering converting the general SMV model into the MMV model. In this study, a simple transforming model that takes advantage of the correlations among wavelet coefficients is proposed, such that the MMV model can be used for general images rather than only certain special signals. To further enhance the performance of the MMV model, the improved MMV model based on the similarity of image blocks is proposed. Simulation results have shown that the obtained solution matrix can be used in the MMV model, and that the proposed algorithm provides better reconstruction quality than many state-of-the-art algorithms.