Perturbed-attractor particle swarm optimization for image restoration

Perturbed-attractor particle swarm optimization for image restoration

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This chapter addresses the problem of restoration of images degraded by blurring and noise. Image degradation is modeled as the convolution of a low-pass, two-dimensional point spread function with a reference or undegraded image. The image restoration problem is modeled as an optimization problem and a modified particle swarm optimization algorithm is developed. This approach is evaluated using image quality metrics that consider the quality of edges restored as well as the fidelity of the restored image with respect to the undegraded reference image. A novel-edge sharpness metric is also proposed. The new approach is shown to perform well in several image restoration examples.

Chapter Contents:

  • Abstract
  • 24.1 Introduction
  • 24.2 Problem description
  • 24.3 Perturbed-attractor PSO for image restoration
  • 24.4 Restoration quality metrics
  • 24.4.1 Edge sharpness metric
  • 24.4.2 Image fidelity: mutual information
  • 24.4.3 Image fidelity: VIF metric
  • 24.5 Simulation results
  • 24.6 Conclusion
  • Acknowledgments
  • Image appendix
  • References

Inspec keywords: optical transfer function; image restoration; particle swarm optimisation

Other keywords: perturbed-attractor particle swarm optimization; image quality metrics; low-pass convolution; edge sharpness metric; image restoration problem; undegraded reference image; image degradation; two-dimensional point spread function

Subjects: Optimisation techniques; Optimisation techniques; Computer vision and image processing techniques; Optical, image and video signal processing

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