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Multiresolution permutation filter implementations based on acyclic connected graphs.

Marcela D Aguirre1, Kenneth E Barner

  • 1Dept. of Electr. and Comput. Eng., Univ. of Delaware, Newark, DE 19716, USA. maguirre@sawtek.com

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|February 2, 2008
PubMed
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This study introduces graph-based permutation filters, overcoming limitations of traditional methods by adaptively using spatial-rank information. These novel filters offer improved performance and flexibility in signal processing applications.

Area of Science:

  • Signal Processing
  • Image Analysis
  • Nonlinear Filtering

Background:

  • Permutation filters leverage spatial and rank order information from observation samples.
  • Traditional permutation filters face computational challenges due to factorial growth in orderings.
  • Existing M-permutation filters have limitations in sample selection appropriateness.

Purpose of the Study:

  • To develop a more general and adaptive approach to permutation filters.
  • To introduce permutation filter implementations based on acyclic connected graphs.
  • To present a Least-Mean-Square Error (LNE) based optimization for graph structure and filter operation.

Main Methods:

  • Utilized acyclic connected graphs for permutation filter implementation.
  • Developed and analyzed graph-based permutation filter structures.

Related Experiment Videos

  • Applied LNE-based optimization to enhance graph structure and filter operations.

    • Main Results:

    • Graph-based permutation filters allow adaptive utilization of ordering information.
    • The LNE optimization technique demonstrated improved performance.
    • Simulation results validated the advantages of the graph implementation.

    Conclusions:

    • Permutation filters based on acyclic connected graphs offer a more flexible and general approach.
    • The proposed LNE-based optimization enhances the efficiency and effectiveness of these filters.
    • This work advances nonlinear selection filter design for improved signal processing.