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Locally adaptive image denoising by a statistical multiresolution criterion

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  • Hotz, Thomas
  • Marnitz, Philipp
  • Stichtenoth, Rahel
  • Davies, Laurie
  • Kabluchko, Zakhar
  • Munk, Axel

Abstract

It is shown how to choose the smoothing parameter in image denoising by a statistical multiresolution criterion, both globally and locally. Using inhomogeneous diffusion and total variation regularization as examples for localized regularization schemes, an efficient method for locally adaptive image denoising is presented. As expected, the smoothing parameter serves as an edge detector in this framework. Numerical examples together with applications in confocal microscopy illustrate the usefulness of the approach.

Suggested Citation

  • Hotz, Thomas & Marnitz, Philipp & Stichtenoth, Rahel & Davies, Laurie & Kabluchko, Zakhar & Munk, Axel, 2012. "Locally adaptive image denoising by a statistical multiresolution criterion," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 543-558.
    • Handle: RePEc:eee:csdana:v:56:y:2012:i:3:p:543-558
    DOI: 10.1016/j.csda.2011.08.018
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    References listed on IDEAS

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    2. De Canditiis, Daniela, 2014. "A frame based shrinkage procedure for fast oscillating functions," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 142-150.

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