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Adaptive Intelligent Single Particle Optimizer Based Image De-noising in Shearlet Domain



Adaptive intelligent single particle optimizer is proposed based on analyzing the deficiency of intelligent single particle optimizer, learning characteristics of particle swarm optimization, and introducing the Cauchy mutation. In the evolution of the algorithm, the particles not only learn from themselves, and can learn from their own historical experience, and finally can decide their velocity and position. Image edge blur is obtained by using the traditional nonlinear diffusion image de-noising method; Shearlet is a new-style multi-scale geometry analysis tool. It creates Shearlet functions, which have different characteristics through zooming, shearing translating and other affine transforming methods and enables its capable of optimally sparse representation. The paper proposed discusses adaptive intelligent single particle optimizer based image de-noising in Shearlet domain. Experimental results show that the method can effectively filter out image noise and better retain edge information, especially to the images containing abundant texture. Meanwhile, the de-noised images have higher Peak Signal to Noise Ratio.



Total Pages: 6
Pages: 661-666


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Volume: 23
Issue: 4
Year: 2017

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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
PREVIOUS DOI PREFIX (with T&F): 10.1080/10798587
InCites Journal IMPACT FACTOR (JIF) Data

2018  0.790
2017  0.652
2016  0.644

Scimago Journal and Country Rank (SJR) Data

2018  0.993
2017  0.655
2016  0.660
SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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