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Research on Algorithm of Correlation Denoising Based on Wavelet Transform

  • Lei Yang
  • Feng Xue
  • Hong hai Wang
  • Hua wei Cheng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 482)

Abstract

Aiming at the problem of noise in the traffic monitoring, especially at night, an image denoising algorithm is proposed. Application of correlation denoising algorithm based on wavelet transform in traffic monitoring. Firstly, Haar wavelet is used as the transform matrix, then image data are denoised. And The similarity of the image is evaluated by the peak signal to noise ratio. Finally, the obtained data is compared with the algorithm of existing adaptive threshold. It is found that the improved algorithm of correlation denoising achieved the expected effect and can denoise the monitoring image of nighttime very well.

Keywords

Correlation denoising Wavelet transform Traffic monitoring 

Notes

Acknowledgements

Project fund:

1. Natural Science Research Project of Anhui Province. Item Number: KJ2017A522;

2. Anhui Sanlian university research fund. Item Number: Yjt16002;

3. Anhui Sanlian university research fund. Item Number: kjzd2016002;

4. Anhui Sanlian university research fund. Item Number: PTZD2017001;

5. Anhui Sanlian university research fund. Item Number: 14zlgc045;

6. Anhui Sanlian university research fund. Item Number: kjyb2016002.

References

  1. 1.
    Zhang L, Bao P (2003) Denoising by spatial correlation thresholding. IEEE Trans Circuits Syst Video Technol 13(6):535–538CrossRefGoogle Scholar
  2. 2.
    Portilla J, Simoncelli EP (2000) Image denoising via adjustment of wavelet coefficient magnitude correlation. In: Proceedings of international conference on image processing. IEEE, vol 3, pp 277–280Google Scholar
  3. 3.
    Zhan DQ, Sun SQ, Zhou Q et al (2004) Wavelet denoising and optimization of two-dimensional correlation IR spectroscopy. Spectrosc Spectral Anal 24(12):1549Google Scholar
  4. 4.
    Portilla J, Strela V, Wainwright MJ et al (2003) Image denoising using scale mixtures of Gaussians in the wavelet domain. IEEE Trans Image Process Publ IEEE Sig Process Soc 12(11):1338MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Sardy S (2000) Minimax threshold for denoising complex signals with waveshrink. IEEE Trans Sig Process 48(4):1023–1028MathSciNetCrossRefGoogle Scholar
  6. 6.
    Buades T, Lou Y, Morel JM et al (2015) A note on multi-image denoising. In: International workshop on local and non-local approximation in image processing. IEEE, pp 1–15Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Lei Yang
    • 1
  • Feng Xue
    • 1
  • Hong hai Wang
    • 1
  • Hua wei Cheng
    • 1
  1. 1.An Hui San Lian UniversityHefeiChina

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