A Study on Image Reconfiguration Algorithm of Compressed Sensing
Abstract: Compressed sensing
theory is a subversion of the traditional theory. The theory obtains data sampling
points while achieves data compression. 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, then choosing three
algorithms (OMP, CoSaMP, and StOMP) as the research. 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. In the three reconstruction algorithms, OMP algorithm has the
best accuracy for image reconstruction. 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.
Author: Yubo Zhang, Dongmei
Wang, Lingling Kan, Panpan Zhao
Journal Code: jptkomputergg170111
