Name
Title | ||
|---|---|---|
Modified particle swarm optimization and fuzzy regularization for pseudo de-convolution of spatially variant blurs |
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/s11042-015-2587-4 | Multimedia Tools Appl. |
Keywords | Field | DocType |
Ill-posed inverse problem, Space variant degradation, Pseudo de-convolution, Fuzzy regularization, Particle swarm optimization, Mathematical morphology | Particle swarm optimization,Singular value decomposition,Pattern recognition,Convolution,Mathematical morphology,Computer science,Fuzzy logic,Multi-swarm optimization,Inverse problem,Artificial intelligence,Initialization | Journal |
Volume | Issue | ISSN |
75 | 11 | 1573-7721 |
Citations | PageRank | References |
1 | 0.35 | 16 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mohsin Bilal | 1 | 15 | 3.12 |
| Hasan Mujtaba | 2 | 22 | 5.32 |
| Muhammad Arfan Jaffar | 3 | 24 | 3.80 |