Multi-Denoising based Impulse Noise Removal from Images using Robust Statistical Features and Genetic Programming

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@Article{Javed:2015:MTA,
  author =       "Syed Gibran Javed and Abdul Majid and 
                 Anwar M. Mirza and Asifullah Khan",
  title =        "Multi-Denoising based Impulse Noise Removal from
                 Images using Robust Statistical Features and Genetic
                 Programming",
  journal =      "Multimedia Tools and Applications",
  year =         "2016",
  volume =       "75",
  number =       "10",
  pages =        "5887--5916",
  month =        may,
  keywords =     "genetic algorithms, genetic programming, Image
                 denoising, Noise detection, Mixed impulse noise, Salt
                 and pepper noise, Impulse burst noise, Statistical
                 features, Robust outlyingness ratio",
  publisher =    "Springer",
  ISSN =         "1380-7501",
  language =     "English",
  URL =          "http://dx.doi.org/10.1007/s11042-015-2554-0",
  DOI =          "doi:10.1007/s11042-015-2554-0",
  size =         "30 pages",
  abstract =     "Recently, several interesting computational
                 intelligence based image denoising techniques have been
                 reported for the removal of either salt and pepper or
                 uniform impulse noise. However, to the best of our
                 knowledge, the difficult challenge of developing a
                 multi denoising method that can remove mixed-impulse
                 noise, uniform impulse, salt and pepper, and
                 impulse-burst noise, has not been reported so far. In
                 this regard, we propose a new noise removal approach
                 called INDE-GP for the removal of multi types of
                 impulse noises. The proposed approach consists of two
                 stages: noise detection stage and removal stage. At
                 first, the impulse noise is localized by a single stage
                 GP detector that exploits various information-rich,
                 rank-ordered and robust statistical features for
                 detection. Next the noise is removed only from the
                 detected noisy pixels by single stage GP estimator.
                 This estimator is developed by exploiting the global
                 learning capability of GP and local statistical
                 measures of noise-free pixels present in the
                 neighbourhood of noisy pixels. The experimental results
                 and comparative analysis with existing denoising
                 techniques show that multi denoising performance of the
                 proposed INDE-GP approach is better both quantitative
                 and qualitative ways.",
  notes =        "Affiliated with Department of Computer and Information
                 Sciences, Pakistan Institute of Engineering and Applied
                 Sciences (PIEAS)

                 The on-line version of this article contains
                 supplementary material, which is available to
                 authorized users.",
}

Genetic Programming entries for Syed Gibran Javed Abdul Majid Anwar M Mirza Asifullah Khan

Citations