期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2016
卷号：14
期号：2A
页码：265-273
DOI：10.12928/telkomnika.v14i2A.4320
语种：English
出版社：Universitas Ahmad Dahlan
摘要：In the issues of signal de-noising, utilize K-SVD and other classical dictionary learning algorithm for sparse decomposition and reconstruction of signal, which cannot effectively eliminate influence of noise. The method suggested by this paper makes some improvement to the classical dictionary learning. Firstly, utilize K-SVD algorithm to make the dictionary learning; then, utilize the method of non-linear least squares to fit each atom in the dictionary and get the revised dictionary; finally, utilize the method of Particle Swarm Optimization to solve the spare representation of signal and get the reconstructed signal at last. It is proved through the experience that the de-noising effect of this paper is obvious superior to the conventional dictionary learning algorithm and is close to the effect of wavelet analytical approach.
其他摘要：In the issues of signal de-noising, utilize K-SVD and other classical dictionary learning algorithm for sparse decomposition and reconstruction of signal, which cannot effectively eliminate influence of noise. The method suggested by this paper makes some improvement to the classical dictionary learning. Firstly, utilize K-SVD algorithm to make the dictionary learning; then, utilize the method of non-linear least squares to fit each atom in the dictionary and get the revised dictionary; finally, utilize the method of Particle Swarm Optimization to solve the spare representation of signal and get the reconstructed signal at last. It is proved through the experience that the de-noising effect of this paper is obvious superior to the conventional dictionary learning algorithm and is close to the effect of wavelet analytical approach.
