Wavelet Based Noise Reduction by Identification of Correlations

  • Anja Borsdorf
  • Rainer Raupach
  • Joachim Hornegger
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


In this paper we present a novel wavelet based method for edge preserving noise reduction. In contrast to most common methods, the algorithm introduced here does not work on single input data. It takes two or more spatially identical images, which are both impaired by noise. Assuming the statistical independence of noise in the different images, correlation computations can be used in order to preserve structures while reducing noise. Different methods for correlation analysis have been investigated, on the one hand based directly on the original input images and on the other hand taking into account the wavelet representation of the input data. The presented approach proves to be suited for the application in computed tomography, where high noise reduction rates of approximately 50% can be achieved without loss of structure information.


Input Image Noise Reduction Wavelet Transformation Difference Image Wavelet Decomposition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Anja Borsdorf
    • 1
    • 2
  • Rainer Raupach
    • 1
    • 2
  • Joachim Hornegger
    • 1
    • 2
  1. 1.Institute of Pattern RecognitionFriedrich-Alexander-UniversityErlangen-Nuremberg
  2. 2.Siemens Medical SolutionsForchheim

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