Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm

Mohsin Bilal; Mudasser F. Wyne; Muhammad Arfan Jaffar

doi:10.1109/CEC.2016.7744113

Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm

Mohsin Bilal, Mudasser F. Wyne, Muhammad Arfan Jaffar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Image restoration is a multivariate-stochastic optimization challenge. In this paper, an improved particle swarm algorithm is proposed for image restoration. The proposed method - Optimal Image Restoration using Improved Particle Swarm Algorithm (OIRIPSA) - is analyzed with an approximated (constrained least square error) and a true cost (mean squared error) measures, respectively. Initial swarm of heuristic solutions is constructed arbitrarily along with problem specific knowledge. An optimistic hyper layer is further integrated to enhance the swarm search procedure by constructing an incipient solution in the neighborhood of the generation's best. OIRIPSA is engendering better restoration than Richardson-Lucy algorithm and a state-of-the-art restoration method.

Original language	English
Title of host publication	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2596-2603
Number of pages	8
ISBN (Electronic)	9781509006229
DOIs	https://doi.org/10.1109/CEC.2016.7744113
State	Published - 14 Nov 2016
Externally published	Yes
Event	2016 IEEE Congress on Evolutionary Computation, CEC 2016 - Vancouver, Canada Duration: 24 Jul 2016 → 29 Jul 2016

Publication series

Name	2016 IEEE Congress on Evolutionary Computation, CEC 2016

Conference

Conference	2016 IEEE Congress on Evolutionary Computation, CEC 2016
Country/Territory	Canada
City	Vancouver
Period	24/07/16 → 29/07/16

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10.1109/CEC.2016.7744113

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Bilal, M., Wyne, M. F., & Jaffar, M. A. (2016). Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm. In 2016 IEEE Congress on Evolutionary Computation, CEC 2016 (pp. 2596-2603). Article 7744113 (2016 IEEE Congress on Evolutionary Computation, CEC 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CEC.2016.7744113

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title = "Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm",

abstract = "Image restoration is a multivariate-stochastic optimization challenge. In this paper, an improved particle swarm algorithm is proposed for image restoration. The proposed method - Optimal Image Restoration using Improved Particle Swarm Algorithm (OIRIPSA) - is analyzed with an approximated (constrained least square error) and a true cost (mean squared error) measures, respectively. Initial swarm of heuristic solutions is constructed arbitrarily along with problem specific knowledge. An optimistic hyper layer is further integrated to enhance the swarm search procedure by constructing an incipient solution in the neighborhood of the generation's best. OIRIPSA is engendering better restoration than Richardson-Lucy algorithm and a state-of-the-art restoration method.",

author = "Mohsin Bilal and Wyne, \{Mudasser F.\} and Jaffar, \{Muhammad Arfan\}",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE Congress on Evolutionary Computation, CEC 2016 ; Conference date: 24-07-2016 Through 29-07-2016",

year = "2016",

month = nov,

day = "14",

doi = "10.1109/CEC.2016.7744113",

language = "English",

series = "2016 IEEE Congress on Evolutionary Computation, CEC 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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Bilal, M, Wyne, MF & Jaffar, MA 2016, Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm. in 2016 IEEE Congress on Evolutionary Computation, CEC 2016., 7744113, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Institute of Electrical and Electronics Engineers Inc., pp. 2596-2603, 2016 IEEE Congress on Evolutionary Computation, CEC 2016, Vancouver, Canada, 24/07/16. https://doi.org/10.1109/CEC.2016.7744113

Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm. / Bilal, Mohsin; Wyne, Mudasser F.; Jaffar, Muhammad Arfan.
2016 IEEE Congress on Evolutionary Computation, CEC 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 2596-2603 7744113 (2016 IEEE Congress on Evolutionary Computation, CEC 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Wyne, Mudasser F.

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N2 - Image restoration is a multivariate-stochastic optimization challenge. In this paper, an improved particle swarm algorithm is proposed for image restoration. The proposed method - Optimal Image Restoration using Improved Particle Swarm Algorithm (OIRIPSA) - is analyzed with an approximated (constrained least square error) and a true cost (mean squared error) measures, respectively. Initial swarm of heuristic solutions is constructed arbitrarily along with problem specific knowledge. An optimistic hyper layer is further integrated to enhance the swarm search procedure by constructing an incipient solution in the neighborhood of the generation's best. OIRIPSA is engendering better restoration than Richardson-Lucy algorithm and a state-of-the-art restoration method.

AB - Image restoration is a multivariate-stochastic optimization challenge. In this paper, an improved particle swarm algorithm is proposed for image restoration. The proposed method - Optimal Image Restoration using Improved Particle Swarm Algorithm (OIRIPSA) - is analyzed with an approximated (constrained least square error) and a true cost (mean squared error) measures, respectively. Initial swarm of heuristic solutions is constructed arbitrarily along with problem specific knowledge. An optimistic hyper layer is further integrated to enhance the swarm search procedure by constructing an incipient solution in the neighborhood of the generation's best. OIRIPSA is engendering better restoration than Richardson-Lucy algorithm and a state-of-the-art restoration method.

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DO - 10.1109/CEC.2016.7744113

M3 - Conference contribution

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BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

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T2 - 2016 IEEE Congress on Evolutionary Computation, CEC 2016

Y2 - 24 July 2016 through 29 July 2016

ER -

Image restoration by multivariate-stochastic optimization using improved particle swarm algorithm

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