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Laser & Optoelectronics Progress, Volume. 57, Issue 14, 141007(2020)

Improved Non-Local Mean Denoising Algorithm Based on Difference Hash Algorithm

Chunjian Hua1,2、*, Jinke Ma1,2, and Ying Chen3
Author Affiliations
  • 1School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
  • 2Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment & Technology, Wuxi, Jiangsu 214122, China;
  • 3School of Internet of Things Engineering, Jiangnan University, Wuxi, Jiangsu 214122, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (9)
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    References (11)
    Cited By (4)
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    Figures & Tables(9)
    Flow chart of algorithm

    Fig. 1. Flow chart of algorithm

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    Test images. (a) Lena; (b) Cameraman; (c) Lena after adding noise; (d) Cameraman after adding noise

    Fig. 2. Test images. (a) Lena; (b) Cameraman; (c) Lena after adding noise; (d) Cameraman after adding noise

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    Fitting result of σ' and h of Lena image

    Fig. 3. Fitting result of σ' and h of Lena image

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    Fitting result of σ' and h of Cameraman image

    Fig. 4. Fitting result of σ' and h of Cameraman image

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    Denoising results of different algorithms. (a) NLM; (b) GNLM; (c) TNLM; (d) HNLM

    Fig. 5. Denoising results of different algorithms. (a) NLM; (b) GNLM; (c) TNLM; (d) HNLM

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    Image details obtained by different algorithms. (a) NLM; (b) GNLM; (c) TNLM; (d) HNLM

    Fig. 6. Image details obtained by different algorithms. (a) NLM; (b) GNLM; (c) TNLM; (d) HNLM

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    • Table 1. λ and h when PSNR is maximum

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      Table 1. λ and h when PSNR is maximum

      σImagePSNR /dBλh
      10Lena34.720.50.006
      Cameraman35.510.50.006
      15Lena33.060.50.014
      Cameraman33.500.50.014
      20Lena31.700.50.026
      Cameraman32.040.50.026
      25Lena30.580.50.044
      Cameraman30.710.50.044
      30Lena29.660.50.066
      Cameraman29.580.50.064
      35Lena28.860.50.092
      Cameraman28.430.50.090

    • Table 2. PSNR and running time of four algorithms under different noises (Lena image)

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      Table 2. PSNR and running time of four algorithms under different noises (Lena image)

      AlgorithmParameterStandard deviation
      102550
      NLMPSNR /dB27.3826.9825.71
      time /s0.450.450.46
      GNLMPSNR /dB34.1130.5327.12
      time /s3.223.173.55
      TNLMPSNR /dB33.2229.4025.99
      time/s3.543.453.51
      HNLMPSNR /dB34.7230.5827.02
      time /s0.630.650.62

    • Table 3. PSNR and running time of four algorithms under different noises (Cameraman image)

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      Table 3. PSNR and running time of four algorithms under different noises (Cameraman image)

      AlgorithmParameterStandard deviation
      102550
      NLMPSNR /dB26.0125.5624.02
      time /s0.410.430.42
      GNLMPSNR /dB34.2030.5225.95
      Time /s3.213.253.26
      TNLMPSNR /dB34.1629.8324.25
      time /s3.523.493.34
      HNLMPSNR /dB35.5130.7125.89
      time /s0.650.690.67
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    Chunjian Hua, Jinke Ma, Ying Chen. Improved Non-Local Mean Denoising Algorithm Based on Difference Hash Algorithm[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141007

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    Paper Information

    Category: Image Processing

    Received: Sep. 9, 2019

    Accepted: Dec. 11, 2019

    Published Online: Jul. 23, 2020

    The Author Email: Chunjian Hua (cjhua@jiangnan.edu.cn)

    DOI:10.3788/LOP57.141007

    Topics

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