Multi-scale Geometric Analysis and Its Application of De-noising

  • Wu Guoning
  • Cao Siyuan
  • Duan Qingquan
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 126)


The essence of multi-scale geometric analysis is to achieve optimal approximation of signal interested. This paper firstly introduces the backgrounds and recent developments of the subject, unveil the impetus behind this root causes. Finally we compare the differences of wavelet transform, contourlet transform and curvelet transform in suppression of random noise, and through practical experiments we confirm that multi-scale geometric analysis are better (sparser) than wavelet in approximating multidimensional signal of interested.


Random Noise Noise Image Image Representation Short Time Fourier Transform Laplacian Pyramid 
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 GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.College of ScienceChina University of PetroleumBeijingChina
  2. 2.College of Geophysics and Information EngineeringChina University of PetroleumBeijingChina
  3. 3.College of Mechanical and Transportation EngineeringChina University of PetroleumBeijingChina

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