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Generalized Extreme Value Filter to Remove Mixed Gaussian-Impulse Noise

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PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

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Abstract

Noise removal in image restoration is an important technique of image processing. In this paper, a new efficient approach is proposed for removing the mixed Gaussian-impulse noise in a color image. The proposed method utilizes the concept of local rank ordered absolute distances to measure similarity between a processing pixel in the small window and their neighborhood pixels in the processing block. The generalized extreme value distribution was employed to estimate weighted averages of the pixels in the processing block for filtering the mixed Gaussian-impulse noise. From the experimental results, our filter has yielded the better results in suppressing high density levels of the mixed noise in the color images than the state-of-the-art denoising methods.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Research Laboratory, Department of Computer Science, Faculty of Science, Prince of Songkla University, Hat Yai, Thailand

    Sakon Chankhachon & Sathit Intajag

Authors
  1. Sakon Chankhachon
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  2. Sathit Intajag
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Corresponding author

Correspondence to Sathit Intajag .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

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Chankhachon, S., Intajag, S. (2016). Generalized Extreme Value Filter to Remove Mixed Gaussian-Impulse Noise. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-42911-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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