Locally edge-adapted distance for image interpolation based on genetic fuzzy system

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Abstract

This study presents a new adaptive scheme for developing kernel-based interpolation methods that simultaneously enhance spatial image resolution and preserve locally detailed edges. A new edge-adapted distance is first estimated according to local gradients information by combining fuzzy theory with genetic learning algorithm. This estimated distance is then employed in place of the original Euclidean distance in various interpolation methods. Additionally, a learning procedure based on genetic algorithm is presented to obtain crucial parameters of the fuzzy system automatically. Experimental results presented in numerical comparisons and in visual observations verify the effectiveness of the proposed adaptive framework for kernel-based interpolation methods.

Original languageEnglish
Pages (from-to)288-297
Number of pages10
JournalExpert Systems with Applications
Volume37
Issue number1
DOIs
StatePublished - Jan 2010

Keywords

  • Fuzzy logic
  • Genetic algorithm
  • Image interpolation
  • Image zooming

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