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Title: Impulse noise removal based on new hybrid conjugate gradient approach (English)
Author: Kimiaei, Morteza
Author: Rostami, Majid
Language: English
Journal: Kybernetika
ISSN: 0023-5954 (print)
ISSN: 1805-949X (online)
Volume: 52
Issue: 5
Year: 2016
Pages: 791-823
Summary lang: English
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Category: math
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Summary: Image denoising is a fundamental problem in image processing operations. In this paper, we present a two-phase scheme for the impulse noise removal. In the first phase, noise candidates are identified by the adaptive median filter (AMF) for salt-and-pepper noise. In the second phase, a new hybrid conjugate gradient method is used to minimize an edge-preserving regularization functional. The second phase of our algorithm inherits advantages of both Dai-Yuan (DY) and Hager-Zhang (HZ) conjugate gradient methods to produce the new direction. The descent property of new direction in each iteration and the global convergence results are established under some standard assumptions. Furthermore, we investigate some conjugate gradient algorithms and the complexity analysis of theirs. Numerical experiments are given to illustrate the efficiency of the new hybrid conjugate gradient (HCGN) method for impulse noise removal. (English)
Keyword: image processing
Keyword: impulse noise
Keyword: unconstrained optimization
Keyword: conjugate gradient method
Keyword: Wolfe conditions
Keyword: complexity analysis
MSC: 03D15
MSC: 68U10
MSC: 90C25
MSC: 90C30
MSC: 90C90
idZBL: Zbl 06674940
idMR: MR3602016
DOI: 10.14736/kyb-2016-5-0791
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Date available: 2017-01-02T13:32:12Z
Last updated: 2018-01-10
Stable URL: http://hdl.handle.net/10338.dmlcz/145969
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