Optimal Choice of Regularization Parameter in Image Denoising

  • Mirko Lucchese
  • Iuri Frosio
  • N. Alberto Borghese
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6978)


The Bayesian approach applied to image denoising gives rise to a regularization problem. Total variation regularizers have been introduced with the motivation of being edge preserving. However we show here that this may not always be the best choice in images with low/medium frequency content like digital radiographs. We also draw the attention on the metric used to evaluate the distance between two images and how this can influence the choice of the regularization parameter. Lastly, we show that hyper-surface regularization parameter has little effect on the filtering quality.


Denoising Total Variation Regularization Bayesian Filtering Digital Radiography 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mirko Lucchese
    • 1
  • Iuri Frosio
    • 1
  • N. Alberto Borghese
    • 1
  1. 1.Applied Intelligent System Laboratory, Computer Science Dept.University of MilanMilanItaly

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