Nonlocal Similarity Image Filtering

  • Yifei Lou
  • Paolo Favaro
  • Stefano Soatto
  • Andrea Bertozzi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)


We exploit the recurrence of structures at different locations, orientations and scales in an image to perform denoising. While previous methods based on “nonlocal filtering” identify corresponding patches only up to translations, we consider more general similarity transformations. Due to the additional computational burden, we break the problem down into two steps: First, we extract similarity invariant descriptors at each pixel location; second, we search for similar patches by matching descriptors. The descriptors used are inspired by scale-invariant feature transform (SIFT), whereas the similarity search is solved via the minimization of a cost function adapted from local denoising methods. Our method compares favorably with existing denoising algorithms as tested on several datasets.


Root Mean Square Scale Invariant Feature Transform Image Denoising Texture Synthesis Denoising Method 
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

  • Yifei Lou
    • 1
  • Paolo Favaro
    • 2
  • Stefano Soatto
    • 1
  • Andrea Bertozzi
    • 1
  1. 1.University of California Los AngelesUSA
  2. 2.Joint Research Institute on Image and Signal processingHeriot-Watt UniversityEdinburghUK

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