Affiliation:
1. College of Electronics and Control Engineering, North China Institute of Aerospace Engineering, Langfang, China
2. College of Mechanical and Electrical Engineering, Hebei Normal University of Science & Technology, Qinhuangdao, China
Abstract
It is difficult to control the balance between artifact suppression and detail preservation. In addition, the information contained in the reconstructed image is limited. For achieving the purpose of less lost information and lower computational complexity in the sampling process, this paper proposed a novel algorithm to realize the image reconstruction using sparse representation. Firstly, the principle of algorithm for sparse representation is introduced, and then the current commonly used reconstruction algorithms are described in detail. Finally, the algorithm can still process the image when the sparsity is unknown by introducing the sparsity theory and dynamically changing the step size to approximate the sparsity. The results explain that the improved algorithm can not only reconstruct the image with unknown sparsity, but also has advantages over other algorithms in reconstruction time. In addition, compared with other algorithms, the reconstruction time of the improved algorithm is the shortest under the same sampling rate.
Publisher
North Atlantic University Union (NAUN)
Subject
Electrical and Electronic Engineering,Signal Processing