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Composite Restoration of Infrared Image Based on Adaptive Threshold Multi-parameter Wavelet

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Pattern Recognition (ACPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14408))

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Abstract

The multiplicative speckle noise and additive background noise of an infrared image are significant elements impacting image quality. To address the issue of image degradation caused by noise superposition and enhance the infrared image quality in terms of noise suppression, a composite restoration method based on adaptive threshold multi-parameter wavelet is proposed. First, based on the noise distribution characteristics of the infrared image, the multiplicative noise in the infrared image is transformed into additive noise, and the image is restored using the wavelet transform coefficient of the converted infrared image. Then, the benefits and drawbacks of soft and hard threshold functions are analysed in depth, and an adaptive double threshold function with adjustable parameters is developed. Finally, a fast non-local means method is used to suppress the effect of background noise on image quality. The experimental results show that the proposed method reduces 111.03 dB on average over the MSE index, 6.67 dB on the PSNR index and 6.92 dB on the SNR index.

This work was supported by National Natural Science Foundation of China (No. 12373100), and the Fundamental Research Funds for the Central Universities.

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Authors and Affiliations

  1. School of Physical Science and Engineering, Tongji University, Shanghai, 200092, China

    Shuai Liu, Peng Chen, Zhengxiang Shen & Zhanshan Wang

  2. Institute of Precision Optical Engineering, Tongji University, Shanghai, 200092, China

    Zhanshan Wang

Authors
  1. Shuai Liu
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  2. Peng Chen
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  3. Zhengxiang Shen
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  4. Zhanshan Wang
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Corresponding author

Correspondence to Zhanshan Wang .

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

  2. The University of Sydney, Sydney, NSW, Australia

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Bejing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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Liu, S., Chen, P., Shen, Z., Wang, Z. (2023). Composite Restoration of Infrared Image Based on Adaptive Threshold Multi-parameter Wavelet. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14408. Springer, Cham. https://doi.org/10.1007/978-3-031-47665-5_31

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  • DOI: https://doi.org/10.1007/978-3-031-47665-5_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-47664-8

  • Online ISBN: 978-3-031-47665-5

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