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Permutation weighted order statistic filter lattices.

G R Arce1, T A Hall, K E Barner

  • 1Dept. of Electr. Eng., Delaware Univ., Newark, DE.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1995
PubMed
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We introduce permutation weighted order statistic (PWOS) filters, a novel class of nonlinear filters. These time-varying filters extend weighted order statistic (WOS) filters and excel in image and video signal processing.

Area of Science:

  • Signal Processing
  • Nonlinear Systems
  • Image and Video Analysis

Background:

  • Weighted order statistic (WOS) filters use sample weights and order statistics for output selection.
  • Existing filters have limitations in modeling complex nonlinear systems and signal distortions.

Purpose of the Study:

  • Introduce and analyze a new class of nonlinear filters: permutation weighted order statistic (PWOS) filters.
  • Extend the capabilities of WOS filters by incorporating temporal and rank-order information.
  • Demonstrate the adaptability and modeling power of PWOS filters.

Main Methods:

  • PWOS filters replicate input samples based on weights determined by temporal-order and rank-order within a window.
  • Filters form a complete lattice structure, allowing a range of complexities.

Related Experiment Videos

  • Realizable by N! piecewise linear threshold logic gates, optimized using stack filter theory.

    • Main Results:

    • PWOS filters are time-varying extensions of WOS filters.
    • The lattice structure provides a spectrum of filters, from simple WOS to complex nonlinear models.
    • Demonstrated advantages in processing image and video signals through simulations.

    Conclusions:

    • PWOS filters offer a powerful and flexible framework for nonlinear signal processing.
    • Their ability to model complex systems makes them suitable for advanced image and video applications.
    • The theoretical framework and simulation results highlight the practical utility of PWOS filters.