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Adaptive Smoothing of Digital Images: The R Package adimpro

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  • Polzehl, Jörg
  • Tabelow, Karsten

Abstract

Digital imaging has become omnipresent in the past years with a bulk of applications ranging from medical imaging to photography. When pushing the limits of resolution and sensitivity noise has ever been a major issue. However, commonly used non-adaptive filters can do noise reduction at the cost of a reduced effective spatial resolution only. Here we present a new package adimpro for R, which implements the propagationseparation approach by (Polzehl and Spokoiny 2006) for smoothing digital images. This method naturally adapts to different structures of different size in the image and thus avoids oversmoothing edges and fine structures. We extend the method for imaging data with spatial correlation. Furthermore we show how the estimation of the dependence between variance and mean value can be included. We illustrate the use of the package through some examples.

Suggested Citation

  • Polzehl, Jörg & Tabelow, Karsten, 2007. "Adaptive Smoothing of Digital Images: The R Package adimpro," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 19(i01).
    • Handle: RePEc:jss:jstsof:v:019:i01
    DOI: http://hdl.handle.net/10.18637/jss.v019.i01
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    References listed on IDEAS

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    1. Jörg Polzehl & Vladimir G. Spokoiny, 2001. "Functional and dynamic magnetic resonance imaging using vector adaptive weights smoothing," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(4), pages 485-501.
    2. J. Polzehl & V. G. Spokoiny, 2000. "Adaptive weights smoothing with applications to image restoration," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(2), pages 335-354.
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    2. Fiebig, Ewelina Marta, 2021. "On data-driven choice of λ in nonparametric Gaussian regression via Propagation–Separation approach," Computational Statistics & Data Analysis, Elsevier, vol. 154(C).
    3. repec:jss:jstsof:44:i12 is not listed on IDEAS
    4. Polzehl, Jörg & Tabelow, Karsten, 2009. "Structural Adaptive Smoothing in Diffusion Tensor Imaging: The R Package dti," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 31(i09).

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