Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-12
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
In order to make full use of nonlocal and local similarity and improve the efficiency and adaptability of the NPB-DL algorithm, this paper proposes a signal reconstruction algorithm based on dictionary learning algorithm combined with structure similarity clustering.
Nonparametric Bayesian for Dirichlet process is firstly introduced into the prior probability modeling of clustering labels, and then, Dirichlet prior distribution is applied to the prior probability of cluster labels so as to ensure the analyticity and conjugation of the probability model.
Experimental results show that the proposed algorithm is not only superior to other comparison algorithms in numerical evaluation indicators but also closer to the original image in terms of visual effects.
American Psychological Association (APA)
Liang, Bin& Liu, Shuxing. 2020. Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1196934
Modern Language Association (MLA)
Liang, Bin& Liu, Shuxing. Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering. Mathematical Problems in Engineering No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1196934
American Medical Association (AMA)
Liang, Bin& Liu, Shuxing. Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1196934
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196934