Impulse noise removal based on new hybrid conjugate gradient approach

Morteza Kimiaei; Majid Rostami

Kybernetika (2016)

  • Volume: 52, Issue: 5, page 791-823
  • ISSN: 0023-5954

Abstract

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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.

How to cite

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Kimiaei, Morteza, and Rostami, Majid. "Impulse noise removal based on new hybrid conjugate gradient approach." Kybernetika 52.5 (2016): 791-823. <http://eudml.org/doc/287565>.

@article{Kimiaei2016,
abstract = {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.},
author = {Kimiaei, Morteza, Rostami, Majid},
journal = {Kybernetika},
keywords = {image processing; impulse noise; unconstrained optimization; conjugate gradient method; Wolfe conditions; complexity analysis; image processing; impulse noise; unconstrained optimization; conjugate gradient method; Wolfe conditions; complexity analysis},
language = {eng},
number = {5},
pages = {791-823},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Impulse noise removal based on new hybrid conjugate gradient approach},
url = {http://eudml.org/doc/287565},
volume = {52},
year = {2016},
}

TY - JOUR
AU - Kimiaei, Morteza
AU - Rostami, Majid
TI - Impulse noise removal based on new hybrid conjugate gradient approach
JO - Kybernetika
PY - 2016
PB - Institute of Information Theory and Automation AS CR
VL - 52
IS - 5
SP - 791
EP - 823
AB - 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.
LA - eng
KW - image processing; impulse noise; unconstrained optimization; conjugate gradient method; Wolfe conditions; complexity analysis; image processing; impulse noise; unconstrained optimization; conjugate gradient method; Wolfe conditions; complexity analysis
UR - http://eudml.org/doc/287565
ER -

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