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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Bibliographic Details
Published in:Multimedia tools and applications 2016-06, Vol.75 (11), p.6533-6548
Main Authors: Bilal, Mohsin, Mujtaba, Hasan, Jaffar, Muhammad Arfan
Format: Article
Language:English
Subjects:
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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Summary: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.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-015-2587-4