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Laser & Optoelectronics Progress, Volume. 58, Issue 6, 610018(2021)

NVST Image Denoising Based on Self-Supervised Deep Learning

Lu Xianwei1, Liu Hui2, and Shang Zhenhong1,3、*
Author Affiliations
  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
  • 2Yunnan Observatories, Chinese Academy of Sciences, Kunming, Yunnan 650216, China
  • 3Yunnan Key Laboratory of Artificial Intelligence, Kunming University of Science and Technology, Kunming, Yunnan 650500, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (8)
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    References (29)
    Cited By (2)
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    Figures & Tables(8)
    Blind spot convolution network with four branches

    Fig. 1. Blind spot convolution network with four branches

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    Convolution blind spot network after optimizing branch

    Fig. 2. Convolution blind spot network after optimizing branch

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    Blind spot receptive domain. (a)Upper half plane;(b) left half plane; (c)lower half plane;(d) right half plane; (e)complete receptive region with blind spots after combination

    Fig. 3. Blind spot receptive domain. (a)Upper half plane;(b) left half plane; (c)lower half plane;(d) right half plane; (e)complete receptive region with blind spots after combination

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    Denoising results of each method. (a) Original images; (b) ground truth; (c) noise interference; (d)proposed algorithm; (e) CBSN; (f) Noise2Void; (g) BM3D; (h) Gaussian filter; (i) mean filter

    Fig. 4. Denoising results of each method. (a) Original images; (b) ground truth; (c) noise interference; (d)proposed algorithm; (e) CBSN; (f) Noise2Void; (g) BM3D; (h) Gaussian filter; (i) mean filter

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    Correlation diagrams. (a) Proposed algorithm;(b)CBSN; (c)Noise2Void; (d)BM3D; (e)Gaussian filter; (f)mean filter

    Fig. 5. Correlation diagrams. (a) Proposed algorithm;(b)CBSN; (c)Noise2Void; (d)BM3D; (e)Gaussian filter; (f)mean filter

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    Power spectra

    Fig. 6. Power spectra

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    Denoising results of real sun image. (a) Real images; (b)proposed algorithm; (c) CBSN; (d) Noise2Void; (e) BM3D; (f) Gaussian filter; (g) mean filter

    Fig. 7. Denoising results of real sun image. (a) Real images; (b)proposed algorithm; (c) CBSN; (d) Noise2Void; (e) BM3D; (f) Gaussian filter; (g) mean filter

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    • Table 1. Average PSNR and SSIM of 20 groups of test images for each method

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      Table 1. Average PSNR and SSIM of 20 groups of test images for each method

      Noise levelMethodKnown σAverage
      PSNRSSIM
      σ= 10BM3DYes37.660.9214
      Gaussian filterYes37.390.9192
      Mean filterNo35.160.8501
      Noise2VoidNo38.210.9389
      CBSNNo39.300.9471
      ProposedNo40.140.9592
      σ= 25BM3DYes33.510.8094
      Gaussian filterYes33.200.8034
      Mean filterNo31.660.6633
      Noise2VoidNo33.750.8522
      CBSNNo34.620.8856
      ProposedNo35.450.8914
      σ= 40BM3DYes32.330.7485
      Gaussian filterYes30.970.6619
      Mean filterNo29.330.4865
      Noise2VoidNo31.770.7962
      CBSNNo33.020.8255
      ProposedNo33.690.8370
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    Lu Xianwei, Liu Hui, Shang Zhenhong. NVST Image Denoising Based on Self-Supervised Deep Learning[J]. Laser & Optoelectronics Progress, 2021, 58(6): 610018

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    Paper Information

    Category: Image Processing

    Received: Aug. 1, 2020

    Accepted: --

    Published Online: Mar. 16, 2021

    The Author Email: Zhenhong Shang (shangzhenhong@126.com)

    DOI:10.3788/LOP202158.0610018

    Topics

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