Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs

We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blu...

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Vydané v:	Multimedia tools and applications Ročník 75; číslo 11; s. 6533 - 6548
Hlavní autori:	Bilal, Mohsin, Mujtaba, Hasan, Jaffar, Muhammad Arfan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.06.2016 Springer Nature B.V
Predmet:	Additives Algorithms Analysis Blurring Computation Computer Communication Networks Computer Science Data Structures and Information Theory Fuzzy logic Inverse problems Mathematical analysis Morphology Multimedia computer applications Multimedia Information Systems Neural networks Noise Optimization Regularization Regularization methods Space telescopes Special Purpose and Application-Based Systems Stars & galaxies Studies Swarm intelligence Wavelet transforms Fuzzy regularization Pseudo de-convolution Space variant degradation Particle swarm optimization Mathematical morphology Ill-posed inverse problem
ISSN:	1380-7501, 1573-7721
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Abstract	We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.
AbstractList	We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches. We propose a modified particle swarm optimization (MPSO) based method for Pseudo De-convolution of the ill-posed inverse problem namely, the space-variant image degradation (SVD). In this paper, SVD is simulated by the pseudo convolution of different sub-regions of the image with different known blurring kernels and additive random noise with unknown variance. Two heuristic modifications are proposed in PSO: 1) Initialization of the swarm and 2) Mutation of the global best. Fuzzy logic is applied for the computation of regularization parameter (RP) to cater for the sensitivity of the problem. The computation of RP is crucial due to the additive noise in the SVD image. Thus mathematical morphology (MM) is applied for better extraction of spatial activity from the distorted image. The performance of the proposed method is evaluated with different test images and noise powers. Comparative analysis demonstrates the superiority of proposed restoration, in terms of quantitative measures, over well-known existing and state-of-the-art SVD approaches.
Author	Mujtaba, Hasan Jaffar, Muhammad Arfan Bilal, Mohsin
Author_xml	– sequence: 1 givenname: Mohsin surname: Bilal fullname: Bilal, Mohsin email: mohsin.bilal@nu.edu.pk organization: Computer Science Department, National University of Computer And Emerging Sciences – sequence: 2 givenname: Hasan surname: Mujtaba fullname: Mujtaba, Hasan organization: Computer Science Department, National University of Computer And Emerging Sciences – sequence: 3 givenname: Muhammad Arfan surname: Jaffar fullname: Jaffar, Muhammad Arfan organization: College of Computer and Information Systems, Al Imam Mohammad Ibn Saud Islamic University (IMSIU)
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Cites_doi	10.1109/NABIC.2009.5393754 10.1007/11550518_60 10.1007/s11042-012-1172-3 10.1007/978-3-540-74272-2_112 10.1109/29.1641 10.1109/IEMBS.2005.1615986 10.1364/OE.14.001767 10.1007/s11042-012-1015-2 10.1007/s11042-014-1867-8 10.1137/S106482759528507X 10.1137/1.9780898718874 10.1007/978-3-540-72823-8_46 10.1109/TIP.2006.873446 10.1016/j.cam.2008.08.013 10.1007/s11042-014-2063-6 10.1109/72.822518 10.1007/s11042-012-1253-3 10.1364/JOSAA.12.002593 10.1109/83.128030 10.6029/smartcr.2013.04.002 10.1007/978-3-642-24785-9_14 10.1364/AO.36.001766
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References	BardsleyJJefferiesSNagyJPlemmonsRA computational method for the restoration of images with an unknown, spatially-varying blurOpt Express20061451767178210.1364/OE.14.001767 Boden AF, Redding DC, Hanisch RJ, Mo J (1996) Massively parallel spatially-variant maximum likelihood image restoration. In: Jacoby GH, Barnes J (eds) Astronomical data analysis software and systems V. Astronomical society of the pacific conference series, vol 101, p 131 Perry SW (2006) Adaptive image restoration: perception based neural network models and algorithms. PhD thesis, School of Electrical and Information Engineering, University of Sydney, NSW Sharif M, Hussain A, Jaffar M, Choi TS (2014) Fuzzy similarity based non local means filter for rician noise removal. Multimedia tools and applications:1–24. doi:10.1007/s11042-014-1867-8 PerrySGuanLWeight assignment for adaptive image restoration by neural networksIEEE Trans Neural Netw200011115617010.1109/72.822518 Dash R, Majhi B (2009) Particle swarm optimization based regularization for image restoration. In: World congress on nature biologically inspired computing, 2009. NaBIC 2009, pp 1253–1257 FaisalMLantermanADSnyderDLWhiteRLImplementation of a modified Richardson-Lucy method for image restoration on a massively parallel computer to compensate for space-variant point spread of a charge-coupled-device cameraJ Opt Soc Am A1995122593260310.1364/JOSAA.12.002593 Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, 2nd edn. Gatesmark Publishing. chap 5 BiggsDSAndrewsMAcceleration of iterative image restoration algorithmsAppl Opt19973681766177510.1364/AO.36.001766 Hansen PC, Nagy JG, O’leary DP (2006) Deblurring images: matrices, spectra, and filtering. Society for industrial and applied mathematics, Philadelphia, PA 19104–2688 Zhao Y, Gui W, Chen Z, Tang J, Li L (2005) Medical images edge detection based on mathematical morphology. In: Proceedings of the 2005 IEEE engineering in medicine and biology 27th annual conference, pp 1–4 BilalMHussainAJaffarMChoiTSMirzaAEstimation and optimization based ill-posed inverse restoration using fuzzy logicMultimedia Tools and Applications20146931067108710.1007/s11042-012-1172-3 MasoodSHussainAJaffarMChoiTSIntelligent noise detection and filtering using neuro-fuzzy systemMultimedia Tools and Applications20136319310510.1007/s11042-012-1015-2 Bilal M, Rehman MSu, Jaffar MA (2013) Evolutionary reconstruction: image restoration for space variant degradation. Smart Computing Review 3(4):220–232 Sun Z, Li E, Zhang J, Gao X (2011) A regularized image restoration algorithm based on improved hybrid particle swarm optimization. In: 2011 6th International forum on strategic technology (IFOST), vol 2, pp 725–728 Welk M, Theis D,Weickert J (2005) Variational deblurring of images with uncertain and spatially variant blurs. In: Kropatsch W, Sablatnig R, Hanbury A (eds) Pattern recognition. Lecture notes in computer science, vol 3663. Springer, Berlin Heidelberg, pp 485–492 ZhouYTChellappaRVaidAJenkinsBImage restoration using a neural networkIEEE Trans Acoust Speech Signal Process19883671141115110.1109/29.16410652.68100 PaikJKatsaggelosAImage restoration using a modified hopfield networkIEEE Trans Image Process199211496310.1109/83.128030 Kober V, Agis J (2007) Space-variant restoration with sliding discrete cosine transform. In: Kropatsch W, Kampel M, Hanbury A (eds) Computer analysis of images and patterns. Lecture notes in computer science, vol 4673. Springer, Berlin Heidelberg, pp 903–911 Klapp I, Sochen N, Mendlovic D (2012) Deblurring space-variant blur by adding noisy image. 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References_xml	– reference: GuXGaoLA new method for parameter estimation of edge-preserving regularization in image restorationJ Comput Appl Math20092252478486249471810.1016/j.cam.2008.08.0131156.94006 – reference: ZiaSJaffarMMirzaAChoiTSRician noise removal from mr images using novel adapted selective non-local means filterMultimedia Tools and Applications201472111910.1007/s11042-012-1253-3 – reference: Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB, 2nd edn. Gatesmark Publishing. chap 5 – reference: Boden AF, Redding DC, Hanisch RJ, Mo J (1996) Massively parallel spatially-variant maximum likelihood image restoration. In: Jacoby GH, Barnes J (eds) Astronomical data analysis software and systems V. Astronomical society of the pacific conference series, vol 101, p 131 – reference: Sharif M, Hussain A, Jaffar M, Choi TS (2014) Fuzzy similarity based non local means filter for rician noise removal. Multimedia tools and applications:1–24. doi:10.1007/s11042-014-1867-8 – reference: ZhouYTChellappaRVaidAJenkinsBImage restoration using a neural networkIEEE Trans Acoust Speech Signal Process19883671141115110.1109/29.16410652.68100 – reference: MignotteMA segmentation based regularization term for image deconvolutionIEEE Trans Image Process2006151973198410.1109/TIP.2006.873446 – reference: BiggsDSAndrewsMAcceleration of iterative image restoration algorithmsAppl Opt19973681766177510.1364/AO.36.001766 – reference: MasoodSHussainAJaffarMChoiTSIntelligent noise detection and filtering using neuro-fuzzy systemMultimedia Tools and Applications20136319310510.1007/s11042-012-1015-2 – reference: Bar L, Sochen N, Kiryati N (2007) Restoration of images with piecewise space-variant blur. In: 1st International conference on Scale space and variational methods in computer vision, LNCS, vol 4485. Springer, Berlin, Heidelberg, pp 533–544 – reference: Welk M, Theis D,Weickert J (2005) Variational deblurring of images with uncertain and spatially variant blurs. In: Kropatsch W, Sablatnig R, Hanbury A (eds) Pattern recognition. Lecture notes in computer science, vol 3663. Springer, Berlin Heidelberg, pp 485–492 – reference: PaikJKatsaggelosAImage restoration using a modified hopfield networkIEEE Trans Image Process199211496310.1109/83.128030 – reference: BardsleyJJefferiesSNagyJPlemmonsRA computational method for the restoration of images with an unknown, spatially-varying blurOpt Express20061451767178210.1364/OE.14.001767 – reference: Dash R, Majhi B (2009) Particle swarm optimization based regularization for image restoration. In: World congress on nature biologically inspired computing, 2009. NaBIC 2009, pp 1253–1257 – reference: BilalMHussainAJaffarMChoiTSMirzaAEstimation and optimization based ill-posed inverse restoration using fuzzy logicMultimedia Tools and Applications20146931067108710.1007/s11042-012-1172-3 – reference: Klapp I, Sochen N, Mendlovic D (2012) Deblurring space-variant blur by adding noisy image. In: Bruckstein A, Haar Romeny B, Bronstein A, Bronstein M (eds) Scale space and variational methods in computer vision. Lecture notes in computer science, vol 6667. Springer, Berlin Heidelberg, pp 157–168 – reference: Sun Z, Li E, Zhang J, Gao X (2011) A regularized image restoration algorithm based on improved hybrid particle swarm optimization. In: 2011 6th International forum on strategic technology (IFOST), vol 2, pp 725–728 – reference: Hansen PC, Nagy JG, O’leary DP (2006) Deblurring images: matrices, spectra, and filtering. Society for industrial and applied mathematics, Philadelphia, PA 19104–2688 – reference: NagyJGO’LearyDPRestoring images degraded by spatially variant blurSIAM J Sci Comput199819410631082161429510.1137/S106482759528507X0919.65091 – reference: Bilal M, Rehman MSu, Jaffar MA (2013) Evolutionary reconstruction: image restoration for space variant degradation. Smart Computing Review 3(4):220–232 – reference: Lucena M, Martnez-Carrillo A, Fuertes J, Carrascosa F, Ruiz A (2014) Decision support system for classifying archaeological pottery profiles based on mathematical morphology. 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SubjectTerms	Additives Algorithms Analysis Blurring Computation Computer Communication Networks Computer Science Data Structures and Information Theory Fuzzy logic Inverse problems Mathematical analysis Morphology Multimedia computer applications Multimedia Information Systems Neural networks Noise Optimization Regularization Regularization methods Space telescopes Special Purpose and Application-Based Systems Stars & galaxies Studies Swarm intelligence Wavelet transforms
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Title	Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs
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