• Laser & Optoelectronics Progress
  • Vol. 58, Issue 22, 2210016 (2021)
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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    DOI: 10.3788/LOP202158.2210016 Cite this Article Set citation alerts
    Bin Fang, Jiayi Chen. Wavelet Threshold Denoising Algorithm for Impulse Noise Removal[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210016 Copy Citation Text show less
    PSNR corresponding to different k
    Fig. 1. PSNR corresponding to different k
    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
    Experimental images. (a) hill; (b) cameraman; (c) mr; (d) xray
    Fig. 3. Experimental images. (a) hill; (b) cameraman; (c) mr; (d) xray
    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
    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
    PSNR of denoising images with different algorithms. (a) cameraman; (b) xray
    Fig. 6. PSNR of denoising images with different algorithms. (a) cameraman; (b) xray
    EPI of denoising images with different algorithms. (a) cameraman; (b) xray
    Fig. 7. EPI of denoising images with different algorithms. (a) cameraman; (b) xray
    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
    Table 1. Processing time of different denoising algorithms unit: s
    Bin Fang, Jiayi Chen. Wavelet Threshold Denoising Algorithm for Impulse Noise Removal[J]. Laser & Optoelectronics Progress, 2021, 58(22): 2210016
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