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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

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Published on: July 28, 2013

Combined curvelet shrinkage and nonlinear anisotropic diffusion.

Jianwei Ma1, Gerlind Plonka

  • 1Laboratoire LMC-IMAG, University Joseph Fourier, 38041 Grenoble Cedex 9, France. jma@tsinghua.edu.cn

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
|September 6, 2007
PubMed
Summary

This study introduces a new diffusion-based curvelet shrinkage method for image denoising. The technique effectively preserves edges and details while reducing artifacts, outperforming existing methods in experiments.

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Area of Science:

  • Image processing
  • Signal processing
  • Applied mathematics

Background:

  • Image denoising is crucial for various applications.
  • Existing methods often struggle with preserving image details and edges.
  • Artifacts like pseudo-Gibbs and curvelet-like patterns can degrade denoised image quality.

Purpose of the Study:

  • To propose a novel diffusion-based curvelet shrinkage method for discontinuity-preserving image denoising.
  • To address and suppress pseudo-Gibbs and curvelet-like artifacts.
  • To enhance the recovery of image edges and important details.

Main Methods:

  • Utilizing a new tight frame of curvelets combined with a nonlinear diffusion scheme.
  • Applying projected total variation diffusion to processed shrinkage results.
  • Employing constrained projection to modify insignificant curvelet coefficients or high-frequency components.

Main Results:

  • The proposed method demonstrates effective discontinuity preservation during denoising.
  • Suppression of pseudo-Gibbs and curvelet-like artifacts was achieved.
  • Experiments showed superior performance in recovering edge shapes and detailed components compared to existing methods.

Conclusions:

  • The diffusion-based curvelet shrinkage method offers a robust solution for image denoising.
  • It excels at preserving essential image features like edges and details.
  • The method shows significant advantages over conventional techniques for both smooth and textured images.