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Deterministic edge-preserving regularization in computed imaging.

P Charbonnier1, L Blanc-Feraud, G Aubert

  • 1CNRS-UNSA, Univ. de Nice-Sophia Antipolis, Valbonne.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|January 1, 1997
PubMed
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This study introduces ARTUR, an edge-preserving image reconstruction algorithm. ARTUR uses an auxiliary variable to maintain image discontinuities, improving regularization in image processing.

Area of Science:

  • Image Processing
  • Computational Imaging
  • Medical Imaging

Background:

  • Image processing often requires regularization for ill-posed problems.
  • Standard regularization methods can smooth important image edges.
  • Preserving edges is crucial for accurate image analysis.

Purpose of the Study:

  • To develop an edge-preserving regularization method for image processing.
  • To introduce an auxiliary variable for discontinuity marking and smoothing prevention.
  • To propose an efficient optimization strategy for image reconstruction.

Main Methods:

  • Designed conditions for edge-preserving regularization.
  • Introduced an auxiliary variable to mark discontinuities and create a half-quadratic criterion.

Related Experiment Videos

  • Developed the ARTUR algorithm using alternate minimizations on the image and auxiliary variable.

    • Main Results:

    • The auxiliary variable successfully preserves image discontinuities.
    • The half-quadratic nature of the criterion simplifies optimization.
    • The ARTUR algorithm demonstrates effective image reconstruction with preserved edges.

    Conclusions:

    • ARTUR is an effective edge-preserving reconstruction algorithm.
    • The proposed method enhances regularization by preserving image discontinuities.
    • ARTUR has broad applicability in various image processing fields, including 2D single photon emission tomography.