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A FAST MAJORIZE MINIMIZE ALGORITHM FOR HIGHER DEGREE TOTAL VARIATION REGULARIZATION.

Yue Hu1, Sathish Ramani2, Mathews Jacob3

  • 1Department of Electrical and Computer Engineering, University of Rochester, NY, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging
|March 26, 2014
PubMed
Summary
This summary is machine-generated.

This study introduces a faster algorithm for image recovery using higher degree total variation (HDTV) penalties. The new method significantly speeds up computations and reduces artifacts in biomedical imaging.

Keywords:
Higher degree total variationcompressed sensingmajorize minimize

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Area of Science:

  • Medical Imaging
  • Computational Science
  • Image Processing

Background:

  • Image recovery is crucial for biomedical inverse problems.
  • Classical total variation (TV) schemes can produce patchy artifacts.
  • Higher Degree Total Variation (HDTV) penalties offer improved image quality.

Purpose of the Study:

  • To develop a computationally efficient algorithm for image recovery problems.
  • To implement and evaluate the performance of a novel majorize-minimize algorithm for HDTV regularization.
  • To compare the new algorithm against existing methods in terms of speed and artifact reduction.

Main Methods:

  • Introduction of a novel majorize-minimize algorithm.
  • Application of anisotropic HDTV penalties, defined as the L1 semi-norm of directional image derivatives.
  • Comparison with previous iterative reweighted algorithms.

Main Results:

  • The novel algorithm achieves an approximate ten-fold speedup compared to previous methods.
  • Reconstructions are free of patchy artifacts common in classical TV schemes.
  • The algorithm demonstrates run times comparable to state-of-the-art total variation regularization schemes.

Conclusions:

  • The proposed majorize-minimize algorithm offers a significant speed improvement for HDTV-regularized image recovery.
  • HDTV regularization with the new algorithm enhances image quality in biomedical applications by reducing artifacts.
  • This efficient approach advances the field of computational imaging and inverse problem solving.