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Image Denoising by Wavelet Transform Based on New Threshold

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Big Data Analytics for Cyber-Physical System in Smart City (BDCPS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1303))

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Abstract

On the basis of two classical hard and soft threshold processing methods, combined with the improved methods mentioned in the literature, a comprehensive threshold processing method is proposed. The threshold function can not only overcome the defects of discontinuity and constant deviation of the traditional threshold, but also adjust the error between the original wavelet coefficient and the thresholding coefficient by adjusting the parameters. Through the comparison of simulation experiments, it is found that the denoising effect of the new threshold function is significantly improved over the traditional threshold denoising effect in terms of visual effect, mean square error, peak signal-to-noise ratio, etc.

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Reference

  1. Yin, Q., Dai, S.: Research on image denoising algorithm based on improved wavelet threshold. Soft Guide 01, 89–91 (2018). (in Chinese)

    Google Scholar 

  2. Zhang, X., Turghunjan, A.T.: Improvement of threshold image denoising algorithm with wavelet transform. Comput. Technol. Dev. 27(03), 81–84 (2017). (in Chinese)

    Google Scholar 

  3. Yang, Z., Zhou, J., Lang, G.: Noise detection and image denoising based on Fractional Calculus. Chin. J. Image Graph. 10, 1418–1429 (2014). (in Chinese)

    Google Scholar 

  4. Yang, L.: Image denoising based on improved wavelet threshold function. J. Chongqing Univ. Technol. (Nat. Sci.) 02, 93–95 (2013). (in Chinese)

    Google Scholar 

  5. Li, X., Qiu, X.: An improved soft threshold image denoising algorithm based on Wavelet Transform. Comput. Technol. Dev. 05, 76–79 (2016). (in Chinese)

    Google Scholar 

  6. Rong, X., Xue, W., Zhu, J.: Application of a new wavelet threshold function in image denoising. Electron. Measur. Technol. 24(5), 84–88 (2016). (in Chinese)

    Google Scholar 

  7. Wang, Z., Li, S.: Fractal wavelet adaptive image denoising based on multivariate statistical model. J. Comput. Sci. 37(06), 1380–1389 (2014). (in Chinese)

    Google Scholar 

  8. Donoho, D.L., Johnstone, J.M.: Ideal spatial via wavelet shrink- age. Biometrika 81, 425–455 (1994)

    Article  MathSciNet  Google Scholar 

  9. Donoho, L.: De-noising by soft thresholding. IEEE Transactions on Information Theory 41(3), 613–627 (1995)

    Article  MathSciNet  Google Scholar 

  10. Donoho, D.L., Johnstone, I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90(432), 1200–1224 (1995)

    Article  MathSciNet  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer and Information Engineering, Zhixing College of Hubei University, Wuhan, 430010, China

    Hua Zhu

  2. Basic Department, Army Logistic University of PLA, Chongqing, 400030, China

    Xiaomei Wang

Authors
  1. Hua Zhu
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  2. Xiaomei Wang
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Corresponding author

Correspondence to Xiaomei Wang .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Oklahoma, Norman, OK, USA

    Mohammed Atiquzzaman

  2. University of Aizu, Fukushima, Japan

    Neil Yen

  3. Shanghai University of Medicine and Health Sciences, Shanghai, China

    Zheng Xu

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Zhu, H., Wang, X. (2021). Image Denoising by Wavelet Transform Based on New Threshold. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_30

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  • DOI: https://doi.org/10.1007/978-981-33-4572-0_30

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-33-4573-7

  • Online ISBN: 978-981-33-4572-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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