Signal Reconstruction Based on Probabilistic Dictionary Learning Combined with Group Sparse Representation Clustering

Bin Liang and Shuxing Liu

Mathematical Problems in Engineering, 2020, vol. 2020, 1-10

Abstract:

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.

Date: 2020
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2020/6615252.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2020/6615252.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6615252

DOI: 10.1155/2020/6615252

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().