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Laser & Optoelectronics Progress, Volume. 55, Issue 3, 031004(2018)

Multiplicative Denoising Method Based on Deep Residual Learning

Ming Zhang, Xiaoqi Lü*, Liang Wu, and Dahua Yu
Author Affiliations
  • School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia 014010, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
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    References (19)
    Cited By (5)
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    Figures & Tables(11)
    Architecture of residual learning

    Fig. 1. Architecture of residual learning

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    Architecture of the CNN

    Fig. 2. Architecture of the CNN

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    Test images used in the experiment

    Fig. 3. Test images used in the experiment

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    Results of different methods for test images under the same noise level. (a) Noise images; (b) Lee method; (c) Frost method; (d) BM3D method; (e) NL method; (f) CNN method

    Fig. 4. Results of different methods for test images under the same noise level. (a) Noise images; (b) Lee method; (c) Frost method; (d) BM3D method; (e) NL method; (f) CNN method

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    Detail comparison of different denoising methods.(a) Lee method; (b) Frost method; (c) BM3D method; (d) NL method; (e) CNN method

    Fig. 5. Detail comparison of different denoising methods.(a) Lee method; (b) Frost method; (c) BM3D method; (d) NL method; (e) CNN method

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    Denoising results of CNN method under different noise levels. (a) σ2=0.02; (b) σ2=0.04;(c) σ2=0.06; (d) σ2=0.08; (e) σ2=0.1

    Fig. 6. Denoising results of CNN method under different noise levels. (a) σ2=0.02; (b) σ2=0.04;(c) σ2=0.06; (d) σ2=0.08; (e) σ2=0.1

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    (a) PSNR and (b) SSIM of different denoising methods versus noise level

    Fig. 7. (a) PSNR and (b) SSIM of different denoising methods versus noise level

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    • Table 1. PSNR of different methods for test images under the same noise leveldB

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      Table 1. PSNR of different methods for test images under the same noise leveldB

      ImageLeeFrostBM3DNLCNN
      Lena26.4025.1420.8828.2230.59
      Baboon23.2122.3520.5422.3326.06
      Barbara23.5723.2021.6623.1628.45
      Boats25.4024.1420.3225.5028.65
      Peppers25.9724.8720.8728.4530.14
      Satellite25.5823.6220.0625.8328.38
      Average25.0223.8920.7225.5828.71

    • Table 2. SSIM of different methods for test images under the same noise level

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      Table 2. SSIM of different methods for test images under the same noise level

      ImageLeeFrostBM3DNLCNN
      Lena0.6470.4960.4010.7330.846
      Baboon0.5640.4830.5170.4510.756
      Barbara0.6000.5100.5590.5870.855
      Boats0.6340.5100.4240.6470.784
      Peppers0.6420.4710.4160.7310.804
      Satellite0.5720.4250.3440.5720.719
      Average0.6100.4820.4440.6200.794

    • Table 3. PSNR of different methods under different noise levelsdB

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      Table 3. PSNR of different methods under different noise levelsdB

      NoiseimageLeeFrostBM3DNLCNN
      σ2=0.0227.7127.5825.0028.9532.99
      σ2=0.0426.3625.0820.8728.1930.66
      σ2=0.0625.3723.6018.8527.5329.25
      σ2=0.0824.5522.5217.5126.9728.19
      σ2=0.1024.0021.6216.4826.4227.18
      Average25.6024.0819.7427.6129.65

    • Table 4. SSIM of different methods under different noise levels

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      Table 4. SSIM of different methods under different noise levels

      NoiseimageLeeFrostBM3DNLCNN
      σ2=0.020.7360.6020.5580.7790.882
      σ2=0.040.6430.4950.4010.7300.843
      σ2=0.060.5830.4320.3320.6920.817
      σ2=0.080.5400.3910.2840.6620.795
      σ2=0.100.5010.3570.2450.6330.774
      Average0.6010.4550.3640.6990.822
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    Ming Zhang, Xiaoqi Lü, Liang Wu, Dahua Yu. Multiplicative Denoising Method Based on Deep Residual Learning[J]. Laser & Optoelectronics Progress, 2018, 55(3): 031004

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    Paper Information

    Category: Image processing

    Received: Sep. 5, 2017

    Accepted: --

    Published Online: Sep. 10, 2018

    The Author Email: Lü Xiaoqi ( lxiaoqi@126.com)

    DOI:10.3788/LOP55.031004

    Topics

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