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

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 Diric...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2020; no. 2020; pp. 1 - 10
Main Authors Liang, Bin, Liu, Shuxing
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
Hindawi Limited
Subjects
Algorithms
Clustering
Conditional probability
Conjugation
Dictionaries
Dirichlet problem
Labels
Machine learning
Mathematical problems
Signal reconstruction
Similarity
Statistical analysis
Visual effects
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2020/6615252