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Image Denoising Method Based on Curvelet Transform in Telemedicine

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Simulation Tools and Techniques (SIMUtools 2020)

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Abstract

To resolve the problems that the traditional image denoising methods are easy to lose details such as edges and textures, a new method of image denoising was proposed. It based on the Curvelet denoising algorithm, using polynomial interpolation threshold method, combining with Wrapping and Cycle spinning techniques to determine the adaptive threshold of each Curvelet coefficient for denoising the medical images. Simulation experiments confirm that the new method reduces the pseudo Gibbs phenomenon, retains the details and texture of the image better, and obtains better visual effects and higher PSNR values.

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Acknowledgement

This work is partly supported by the Science and Technology Project of Jiangsu Provincial Department of Housing and Construction (2019ZD039), Science and Technology Project of Jiangsu Provincial Department of Housing and Construction (2019ZD040).

Author information

Authors and Affiliations

  1. Xuzhou University of Technology, Xuzhou, 221000, Jiangsu, China

    Yang Yu, Dan Li, Kailiang Zhang & Yuan An

  2. Traffic Police Detachment of Xuzhou Public Security Bureau, Xuzhou, 221000, Jiangsu, China

    Likai Wang & Weiwei Liu

Authors
  1. Yang Yu
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  2. Dan Li
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  3. Likai Wang
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  4. Weiwei Liu
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  5. Kailiang Zhang
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  6. Yuan An
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Editor information

Editors and Affiliations

  1. Embry-Riddle Aeronautical University, Daytona Beach, FL, USA

    Houbing Song

  2. School of Astronautics and Aeronautic, UESTC, Chengdu, China

    Dingde Jiang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Yu, Y., Li, D., Wang, L., Liu, W., Zhang, K., An, Y. (2021). Image Denoising Method Based on Curvelet Transform in Telemedicine. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 370. Springer, Cham. https://doi.org/10.1007/978-3-030-72795-6_54

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  • DOI: https://doi.org/10.1007/978-3-030-72795-6_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-72794-9

  • Online ISBN: 978-3-030-72795-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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