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Low-Level Image Processing Based on Interval-Valued Fuzzy Sets and Scale-Space Smoothing

  • Samuel DelepoulleEmail author
  • André Bigand
  • Christophe Renaud
  • Olivier Colot
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 657)

Abstract

In this paper, a new technique based on interval-valued fuzzy sets and scale-space smoothing is proposed for image analysis and restoration. Interval-valued fuzzy sets (IVFS) are associated with type-2 semantic uncertainty that makes it possible to take into account usually ignored (or difficult to manage) stochastic errors during image acquisition. Indeed, the length of the interval (of IVFS) provides a new tool to define a particular resolution scale for scale-space smoothing. This resolution scale is constructed from two smoothed image histograms and is associated with interval-valued fuzzy entropy (IVF entropy). Then, IVF entropy is used for analyzing the image histogram to find the noisy pixels of images and to define an efficient image quality metric. To show the effectiveness of this new technique, we investigate two specific and significant image processing applications: no-reference quality evaluation of computer-generated images and speckle noise filtering.

Keywords

Membership Function Noisy Image Impulse Noise Multiplicative Noise Speckle Noise 
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 International Publishing Switzerland 2017

Authors and Affiliations

  • Samuel Delepoulle
    • 2
    Email author
  • André Bigand
    • 2
  • Christophe Renaud
    • 2
  • Olivier Colot
    • 1
  1. 1.LAGIS-UMR CNRS 8219, Université Lille 1Villeneuve d’Ascq CedexFrance
  2. 2.LISIC-ULCO, 50 rue Ferdinand Buisson - BP 699Calais CedexFrance

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