Magnitude Type Preserving Similarity Measure for Complex Wavelet Based Image Registration

  • Florina-Cristina Calnegru
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8192)


Most of the similarity measures, currently used for image registration, aim to model the relation between the intensities of correspondent pixels. This is true even for some of the similarity measures that are not directly defined on the intensities of the images to be registered, but on some transformed version of those images. A potential problem with this approach is that it relies on the values of intensities, for which in most of the times it is too difficult to predict their pattern of variation. A way to circumvent this problem is to define a similarity measure on the domain of the magnitudes of complex wavelet coefficients, as the magnitudes are less affected by noise than the intensities. This property of robustness to noise allows to predict a certain behavior of the corresponding magnitudes, namely that they will preserve their type. This means that large (small) magnitudes from the complex wavelet transform of one image will correspond to large (small) magnitudes in the complex wavelet transform of the other image. Starting from this constancy in the behavior of complex wavelet magnitudes, we propose a new similarity measure that has sub pixel accuracy, robustness to noise and is faster than the most related known similarity measure.


Similarity Measure Reference Image Image Registration Wavelet Coefficient Type Divergence 
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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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Florina-Cristina Calnegru
    • 1
  1. 1.Department of Computer ScienceUniversity of PitestiPitestiRomania

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