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SAR Image De-noising Based on Generalized Non-local Means in Non-subsample Shearlet Domain

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Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 386))

Abstract

How to suppress and remove the speckle of SAR image has been a hot research issue. Combining the advantages of non-subsample Shearlet transform (NSST) with the generalized non-local means de-noising algorithm, we proposed a new SAR image de-noising algorithm in this paper. This algorithm is appropriate for the characteristics of the speckle noise, so it can improve the quality of de-noised image. Meanwhile, the algorithm holds the characteristics of translational invariance, which can suppress Gibbs phenomenon effectively.

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Acknowledgments

This work was supported by National Natural Science Foundation of China (61572063, 61401308), Natural Science Foundation of Hebei University (2014-303), Natural Science Foundation of Hebei Province (F2016201122), Science research project of Hebei Province (QN2016085, ZC2016040).

Author information

Authors and Affiliations

  1. College of Electronic and Information Engineering, Hebei University, Hebei, China

    Liu Shuaiqi

  2. Key Laboratory of Digital Medical Engineering of Hebei Province, Hebei, China

    Liu Shuaiqi

  3. School of Information Science and Technology, Shijiazhuang Tiedao University, Hebei, China

    Geng Peng

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Shi Mingzhu

  5. Institute of Information Science, Beijing Jiaotong University, Beijing, China

    Fang Jing & Hu Shaohai

Authors
  1. Liu Shuaiqi
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  2. Geng Peng
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  3. Shi Mingzhu
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  4. Fang Jing
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  5. Hu Shaohai
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Corresponding author

Correspondence to Liu Shuaiqi .

Editor information

Editors and Affiliations

  1. University of Texas at Arlington, ARLINGTON, Texas, USA

    Qilian Liang

  2. Tianjin Normal University, Tianjin, China

    Jiasong Mu

  3. Tianjin Normal University, Tianjin, China

    Wei Wang

  4. College of Physical and Electronic Infor, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Shuaiqi, L., Peng, G., Mingzhu, S., Jing, F., Shaohai, H. (2016). SAR Image De-noising Based on Generalized Non-local Means in Non-subsample Shearlet Domain. In: Liang, Q., Mu, J., Wang, W., Zhang, B. (eds) Proceedings of the 2015 International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 386. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-49831-6_23

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  • DOI: https://doi.org/10.1007/978-3-662-49831-6_23

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-49829-3

  • Online ISBN: 978-3-662-49831-6

  • eBook Packages: EngineeringEngineering (R0)

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