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DOI: 10.14569/IJARAI.2013.021002
PDF

Noise Suppressing Edge Enhancement Based on Genetic Algorithm Taking Into Account Complexity of Target Images Measured with Fractal Dimension

Author 1: Kohei Arai

International Journal of Advanced Research in Artificial Intelligence(IJARAI), Volume 2 Issue 10, 2013.

  • Abstract and Keywords
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Abstract: Method for noise suppressing edge enhancement based on genetic algorithm taking into account complexity of target images measured with fractal dimension is proposed. Through experiments with satellite remote sensing imagery data with additive noise, it is found that the proposed method shows appropriate edge enhancing performance with suppressing the additive noise in accordance with complexity of target images. It is also found that the proposed method requires a small computer resources in comparison to the method based on Simulated Annealing: SA.

Keywords: edge enhancement; fractal dimension; genetic algorithm; simulated annealing; remote sensing satellite imagery

Kohei Arai , “Noise Suppressing Edge Enhancement Based on Genetic Algorithm Taking Into Account Complexity of Target Images Measured with Fractal Dimension” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 2(10), 2013. http://dx.doi.org/10.14569/IJARAI.2013.021002

@article{2013,
title = {Noise Suppressing Edge Enhancement Based on Genetic Algorithm Taking Into Account Complexity of Target Images Measured with Fractal Dimension},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2013.021002},
url = {http://dx.doi.org/10.14569/IJARAI.2013.021002},
year = {2013},
publisher = {The Science and Information Organization},
volume = {2},
number = {10},
author = {Kohei Arai }
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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