Research on Greedy Reconfiguration Algorithm of Compressed Sensing Based on Image

Abstract

Compressed sensing theory is a subversion of the traditional theory. The main content of this thesis is reconstruction algorithm. It’s the key of the compressed sensing theory, which directly determines the quality of reconstructed signal, reconstruction speed and application effect. In this paper, we have studied the theory of compressed sensing and the existing reconstruction algorithms. On the basis of summarizing the existing algorithms and models, we analyze the results such as PSNR, relative error, matching ratio and running time of them from image signal respectively. The convergence speed of CoSaMP algorithm is faster than that of the OMP algorithms, but it depends on sparsity K quietly. StOMP algorithm on image reconstruction effect is the best, and the convergence speed is also the fastest. Sadly, its accuracy is not as good as that of the OMP algorithm.