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Laser & Optoelectronics Progress, Volume. 58, Issue 22, 2210016(2021)

Wavelet Threshold Denoising Algorithm for Impulse Noise Removal

Bin Fang1 and Jiayi Chen2、*
Author Affiliations
  • 1School of Information Engineering, Guangzhou City Construction College, Guangzhou, Guangdong 510925, China
  • 2School of Biomedical Engineering, Guangdong Medical University, Zhanjiang, Guangdong 524023, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (25)
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    PSNR corresponding to different k

    Fig. 1. PSNR corresponding to different k

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    Shrinkage functions. (a) Hard threshold function; (b) soft threshold function; (c) differentiable asymptotic shrinkage function

    Fig. 2. Shrinkage functions. (a) Hard threshold function; (b) soft threshold function; (c) differentiable asymptotic shrinkage function

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    Experimental images. (a) hill; (b) cameraman; (c) mr; (d) xray

    Fig. 3. Experimental images. (a) hill; (b) cameraman; (c) mr; (d) xray

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    Denoising results of different algorithms on image mr. (a) Original image; (b) AMWMF; (c) MFFOF; (d) ASMF; (e) ADWMF; (f) IMF; (g) ENA; (h) WTDA

    Fig. 4. Denoising results of different algorithms on image mr. (a) Original image; (b) AMWMF; (c) MFFOF; (d) ASMF; (e) ADWMF; (f) IMF; (g) ENA; (h) WTDA

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    Denoising results of different algorithms on image hill. (a) Original image; (b) AMWMF; (c) MFFOF; (d) ASMF; (e) ADWMF; (f) IMF; (g) ENA; (h) WTDA

    Fig. 5. Denoising results of different algorithms on image hill. (a) Original image; (b) AMWMF; (c) MFFOF; (d) ASMF; (e) ADWMF; (f) IMF; (g) ENA; (h) WTDA

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    PSNR of denoising images with different algorithms. (a) cameraman; (b) xray

    Fig. 6. PSNR of denoising images with different algorithms. (a) cameraman; (b) xray

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    EPI of denoising images with different algorithms. (a) cameraman; (b) xray

    Fig. 7. EPI of denoising images with different algorithms. (a) cameraman; (b) xray

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    • Table 1. Processing time of different denoising algorithms unit: s

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      Table 1. Processing time of different denoising algorithms unit: s

      Noise density10%20%30%40%50%60%70%80%90%
      AMWMF1.622.042.533.033.614.104.665.205.89
      MFFOF3.234.906.307.799.4011.4013.6017.2022.40
      ASMF1.753.424.826.318.1210.2012.4016.0021.20
      ADWMF17.3034.3066.20102.00170.00241.00300.00340.00382.00
      IMF0.5310.302.413.144.145.076.078.8510.00
      ENA4.215.025.796.136.787.447.859.3510.10
      WTDA1.122.083.054.055.056.037.098.299.97
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    Bin Fang, Jiayi Chen. Wavelet Threshold Denoising Algorithm for Impulse Noise Removal[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210016

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    Paper Information

    Category: Image Processing

    Received: Jan. 11, 2021

    Accepted: Feb. 17, 2021

    Published Online: Nov. 5, 2021

    The Author Email: Jiayi Chen (beyond38@163.com)

    DOI:10.3788/LOP202158.2210016

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology