This method removes noise from images using wavelet transformations or total variation. The process of denoising an image with wavelet transformations can be broken down into the following three parts:

  • Calculate the wavelet transform of the noisy image.
    The wavelet transformation can be calculated by the method Real Wavelets and Complex Wavelets.
  • Modify the noisy wavelet coefficients.
    This is done by using bivariate shrinkage with local variance estimation (thresholding). [Bivariate Shrinkage with Local Variance Estimator, Levent Sendur and Ivan W. Selesnick, IEEE Signal Processing Letters, Vol. 9, No. 12, December 2002]
  • Compute the inverse transform using the threshold coefficients.

Parameter

Description

Method

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Complex Wavelets

The Dual Tree Complex Wavelet transform provides better results due to the fact that it is nearly direction invariant and makes more directional sub bands available. The results will be less prone to block-artefacts. However, this method is computationally more intense and therefore takes longer.

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Real Wavelets

The real wavelet transform only considers three sides (XYZ) and is therefore faster. However, the result can show block artefacts.

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Total Variation

An algorithm based on A. Chambolle, "An Algorithm for Total Variation Minimization and Applications", J. Math. Imaging and Vision 20 (1-2): 89-97, 2004.
It uses the L1 norm for optimization. Typically, total variation generates small plateaus with constant gray values. The size of each of the small plateaus depends on the Strength setting.

Strength

Here you adjust the strength with which the function is applied.