A Two-Stage Fusing Method of Reconstruction Algorithms for Compressed Sensing

Abstract

There are still many algorithms proposed recently to reconstruct signals in Compressed Sensing (CS) setup. However, how to reconstruct sparse signals accurately with fewer measurements and less time is still a problem. It is interesting to observe that algorithms with poor performance do not mean a complete failure, as their support set may include some correct indices that some algorithms with good performance may not find out. Because of this, people proposed some fusing method using modified algorithms and partial support set, however, the reliability of the set is the key to the algorithm, and the modified method of different algorithms and the reconstruction performances of different modified algorithms are still needed to be verified. In this paper, we propose a two-stage fusing method for Compressed Sensing algorithms. From existing algorithms, we choose one as the main algorithm, some other as prior algorithms and run them in different stages. In the first stage we get high-accuracy atomic set from the prior algorithms and in the second stage we use the atomic set as the partial support set and fuse it with the main algorithm adaptively to improve the sparse signal reconstruction. The proposed method is suitable for most CS algorithms which work with different principles. According to the simulation results, the proposed method improves the performance of participating algorithms and is superior to other fusing methods in both reconstruction accuracy and reconstruction time.