A MODIFIED ALGORITHM BASED ON SMOOTHED L0 NORM IN COMPRESSIVE SENSING SIGNAL RECONSTRUCTION

The SL0 algorithm for compressive sensing (CS) reconstruction uses smoothed l(0) norm and introduces a sequence of smoothed functions to approximate the l(0) norm. Therefore, the NP-hard problem of minimization of the l(0) norm can be transferred to a convex optimization problem for smoothed functions. Considering the defects of SL0 algorithm in the iterative process and in order to choose an appropriate the l(0) norm, we use Composite Inverse Proportion Model to approximate the l(0) norm, introduce the thought of the OSL0 algorithm, and combine with the steepest descent method and the gradient projection principle to get the reconstruction signal, a new algorithm called Modified Smoothed l(0) algorithm(MSL0) is proposed. Experimental results show that, under the same test conditions, the MSL0 algorithm is superior to SL0 and other same type algorithms both in the reconstruction quality and the performance.