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A Method of Finding Optimal Parameters of Speckle Noise Reduction Filters

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2020, ruSMART 2020)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 12526))

Abstract

The reduction of multiplicative speckle noise in synthetic aperture radar (SAR) images is an important problem. Many speckle noise reduction filters have been proposed. Most of them have several parameters that control their operation. Finding the optimal values of these parameters is often a non-trivial task. A method of automating the search for optimal parameters is proposed. The method uses two variants of a specially designed test image, original noise free image and the same image but with speckle noise added. Then the Structural Similarity Index (SSIM) metric is used for finding the parameters that make the filtered image as close to the original noise free image as possible. The application of the method is illustrated using the Frost filter applied to various images, but the method can be used for any filter type.

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Authors and Affiliations

  1. Institute of Physics, Nanotechnology and Telecommunications, Peter the Great St. Petersburg Polytechnic University, Saint Petersburg, Russia

    Andrei A. Belov, Vitalii A. Pavlov & Anna A. Tuzova

Authors
  1. Andrei A. Belov
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  2. Vitalii A. Pavlov
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  3. Anna A. Tuzova
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Corresponding author

Correspondence to Anna A. Tuzova .

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Editors and Affiliations

  1. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Olga Galinina

  2. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Sergey Andreev

  3. FRUCT Oy, Helsinki, Finland

    Sergey Balandin

  4. Unit of Electrical Engineering, Tampere University, Tampere, Finland

    Yevgeni Koucheryavy

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Belov, A.A., Pavlov, V.A., Tuzova, A.A. (2020). A Method of Finding Optimal Parameters of Speckle Noise Reduction Filters. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN ruSMART 2020 2020. Lecture Notes in Computer Science(), vol 12526. Springer, Cham. https://doi.org/10.1007/978-3-030-65729-1_12

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  • DOI: https://doi.org/10.1007/978-3-030-65729-1_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65728-4

  • Online ISBN: 978-3-030-65729-1

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