Natural image denoising using evolved local adaptive filters

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@Article{Yan:2014:SP,
  author =       "Ruomei Yan and Ling Shao and Li Liu and Yan Liu",
  title =        "Natural image denoising using evolved local adaptive
                 filters",
  journal =      "Signal Processing",
  year =         "2014",
  volume =       "103",
  pages =        "36--44",
  month =        oct,
  keywords =     "genetic algorithms, genetic programming, Image
                 denoising, Bilateral filter",
  ISSN =         "0165-1684",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0165168413004556",
  DOI =          "doi:10.1016/j.sigpro.2013.11.019",
  size =         "9 pages",
  abstract =     "The coefficients in previous local filters are mostly
                 heuristically optimised, which leads to artifacts in
                 the denoised image when the optimization is not
                 adaptive enough to the image content. Compared to
                 parametric filters, learning-based denoising methods
                 are more capable of tackling the conflicting problem of
                 noise reduction and artifact suppression. In this
                 paper, a patch-based Evolved Local Adaptive (ELA)
                 filter is proposed for natural image denoising. In the
                 training process, a patch clustering is used and the
                 genetic programming (GP) is applied afterwards for
                 determining the optimal filter (linear or nonlinear in
                 a tree structure) for each cluster. In the testing
                 stage, the optimal filter trained beforehand by GP will
                 be retrieved and employed on the input noisy patch. In
                 addition, this adaptive scheme can be used for
                 different noise models. Extensive experiments verify
                 that our method can compete with and outperform the
                 state-of-the-art local denoising methods in the
                 presence of Gaussian or salt-and-pepper noise.
                 Additionally, the computational efficiency has been
                 improved significantly because of the separation of the
                 offline training and the online testing processes.",
}

Genetic Programming entries for Ruomei Yan Ling Shao Li Liu Fiona Yan Liu

Citations