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A novel algorithm for threshold image denoising based on wavelet construction

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Abstract

By analyzing characteristics of wavelet-based image threshold denoising, a biorthogonal wavelet of even symmetry at the zero point with (13-3) filters length and 2/4/6-order vanishing moments is constructed using a filter parameterization method. In light of the disadvantages of global threshold, the self-adaptive hierarchical threshold denoising algorithm is proposed, where the noise decay rate in detail coefficients (detcoef, for short) of wavelet decomposition was employed to calculate hierarchical threshold value. The simulation test verifies that the constructed wavelet has favorable denoising capacity such that image details can be preserved more completely. When combined with the self-adaptive hierarchical threshold denoising algorithm, the wavelet can improve image quality and SNR significantly.

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References

  1. Wang, X.Y., Ou, X.X., Chen, B.W., Kim, M.: Image denoising based on improved wavelet threshold function for wireless camera networks and transmissions. Int. J. Distrib. Sens. Netw. 2, 23 (2015)

    Google Scholar 

  2. Ruan, C.Z., Zhao, D., Jia, W., Chen, C., Chen, Y., Liu, X.: A new image denoising method by combining WT with ICA. Math. Probl Eng 2015, 1–10 (2015)

    Google Scholar 

  3. Zhao, H.H., Lopez, J.F., Martinez, A., Qiao, Z.J.: SAR image denoising based on wavelet packet and median filter. Appl. Mech. Mater. 333–335, 916 (2013)

    Article  Google Scholar 

  4. Xu, D., Sun, L., Luo, J., Liu, Z.: Analysis and denoising of hyperspectral remote sensing image in the curvelet domain. Math. Probl. Eng. 2013, 1943–1997 (2013)

    MathSciNet  MATH  Google Scholar 

  5. Wang, Y., Lei, F., FuAdaptive, G.J.: Denoising algorithms based on wavelet for pool underwater image. Appl. Mech. Mater. 333–335, 1024 (2013)

    Article  Google Scholar 

  6. Kaur, R., Kaur, J.: Comparative analysis of speckle reduction techniques in ultrasound images. Int. J. Comput. Appl. Eng. Sci. 3, 26–8 (2013)

    Google Scholar 

  7. Tian, J., Li, Y., Wang, H.: An image filtering algorithm based on translation invariance wavelet transform. Danjian yu Zhidao Xuebao/J. Projectiles Rocket. Missiles Guidance 32, 140–2 (2012)

    Google Scholar 

  8. Chen, G., Zhu, W.P.: Signal denoising using neighbouring dual-tree complex wavelet coefficients. IET Signal Process. 6, 143–7 (2012)

    Article  MathSciNet  Google Scholar 

  9. Al-geelani, N.A., Piah, M.A.M.: Identification and extraction of surface discharge acoustic emission signals using wavelet neural network. Int. J. Comput. Electr. Eng. 4, 471 (2012)

    Article  Google Scholar 

  10. Mahajan, A., Birajdar, G.: Analysis of blind separation of noisy mixed images based on wavelet thresholding and independent component analysis. Int. J. Eng. Technol. 3, 560 (2011)

    Article  Google Scholar 

  11. Li, Q., Ge, P., Feng, H.J., Xu, Z.H.: Image displacement detection under low illumination using joint transform correlator with wavelet denoising. Appl. Mech. Mater. 128–129, 602 (2011)

    Google Scholar 

  12. Bhutada, G.G., Anand, R.S., Saxena, S.C.: Image enhancement by wavelet-based thresholding neural network with adaptive learning rate. IET Image Process. 5, 573–82 (2011)

    Article  MathSciNet  Google Scholar 

  13. Hu, Y., Zhang, Y., Xiong, C.J., Chen, X.B.: Denoising method with wavelet shrinkage adaptive thresholding and wiener filter. Liaoning Keji Daxue Xuebao (J. Univ. Sci. Technol. Liaoning) 33, 539–42 (2010)

    Google Scholar 

  14. Shi, H.B., Ma, S.L., Han, X.: A new method based on the wavelet transformation of image denoising. Jilin Daxue Xuebao (Lixue Ban)/(J. Jilin Univ.) (Sci. Edi.) 45, 607–610 (2007)

    Google Scholar 

  15. Yang, F., Zhang, Y., Wang, Z., Yang, Q.: Application of wavelet transform-based wiener filtering method to reduce additive noise in apple image. Nongye Jixie Xuebao (Trans. Chin. Soc. Agric. Mach.) 37, 130–133 (2006)

    Google Scholar 

  16. Chan, R.H., Chan, T.F., Shen, L., Shen, Z.: Wavelet algorithms for high-resolution image reconstruction. SIAM J. Sci. Comput. 24, 1408–25 (2003)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

The author was endowed by the Natural Science Foundation of Shaanxi Province (2014JM7297) and Industry University Research Project in Yulin city (2015CXY-21).

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Authors and Affiliations

  1. School of Science, Chang’an University, Xi’an, 710064, China

    Zhang Jianhua, Zhang Jinrong, Song Lin & Wang Jilong

  2. School of Mechanical Engineering, Xi’an Technological University, Xi’an, 710032, China

    Zhu Qiang

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  1. Zhang Jianhua
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  2. Zhu Qiang
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  3. Zhang Jinrong
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  4. Song Lin
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  5. Wang Jilong
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Correspondence to Zhu Qiang.

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Jianhua, Z., Qiang, Z., Jinrong, Z. et al. A novel algorithm for threshold image denoising based on wavelet construction. Cluster Comput 22 (Suppl 5), 12443–12450 (2019). https://doi.org/10.1007/s10586-017-1655-0

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  • DOI: https://doi.org/10.1007/s10586-017-1655-0

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