Dynamic Texture Extraction and Video Denoising

  • Mathieu Lugiez
  • Michel Ménard
  • Abdallah El-Hamidi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5807)


According to recent works, introduced by Y.Meyer [1] the decomposition models based on Total Variation (TV) appear as a very good way to extract texture from image sequences. Indeed, videos show up characteristic variations along the temporal dimension which can be catched in the decomposition framework. However, there are very few works in literature which deal with spatio-temporal decompositions. Thus, we devote this paper to spatio-temporal extension of the spatial color decomposition model. We provide a relevant method to accurately catch Dynamic Textures (DT) present in videos. Moreover, we obtain the spatio-temporal regularized part (the geometrical component), and we distinctly separate the highly oscillatory variations, (the noise). Furthermore, we present some elements of comparison between several models in denoising purpose.


Texture Component Decomposition Model Dynamic Texture Image Decomposition Wavelet Shrinkage 
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 2009

Authors and Affiliations

  • Mathieu Lugiez
    • 1
    • 2
  • Michel Ménard
    • 1
  • Abdallah El-Hamidi
    • 2
  1. 1.L3i - Université de La RochelleFrance
  2. 2.MIA - Université de La RochelleFrance

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