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Laser & Optoelectronics Progress, Volume. 56, Issue 16, 161006(2019)

Image Denoising Using Weighted Nuclear Norm Minimization with Preserving Local Structure

Junrui Lü1, Xuegang Luo1、*, Shifeng Qi1, and Zhenming Peng2
Author Affiliations
  • 1 School of Mathematics and Computer Science, Panzhihua University, Panzhihua, Sichuan 617000, China
  • 2 School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 610054, China
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    Figures & Tables(5)
    Comparison of average values of PNSR and FSIM indexes for Berkeley test dataset images by various algorithms. (a) Comparison result of PNSR values; (b) comparison result of FSIM values

    Fig. 1. Comparison of average values of PNSR and FSIM indexes for Berkeley test dataset images by various algorithms. (a) Comparison result of PNSR values; (b) comparison result of FSIM values

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    Comparison of denoising effects of noised MRI brain slices by various algorithms. (a) MRI slice with noise; (b) denoising effect by RM algorithm; (c) denoising effect by BM3D algorithm; (d) denoising effect by WSNM algorithm; (e) denoising effect by WNNM algorithm; (f) denoising effect by proposed algorithm

    Fig. 2. Comparison of denoising effects of noised MRI brain slices by various algorithms. (a) MRI slice with noise; (b) denoising effect by RM algorithm; (c) denoising effect by BM3D algorithm; (d) denoising effect by WSNM algorithm; (e) denoising effect by WNNM algorithm; (f) denoising effect by proposed algorithm

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    Denoising residual components of Lena image (σ= 40). (a) Residual component of RM algorithm; (b) residual component of BM3D algorithm; (c) residual component of proposed algorithm; (d) residual component of WSNM algorithm; (e) residual component of WNNM algorithm

    Fig. 3. Denoising residual components of Lena image (σ= 40). (a) Residual component of RM algorithm; (b) residual component of BM3D algorithm; (c) residual component of proposed algorithm; (d) residual component of WSNM algorithm; (e) residual component of WNNM algorithm

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    Comparison of denoising details of Male image in salt and pepper noise with the noise density of 50. Figs. 4(c)-(g) are detail parts of Fig. 4(a). (a) Original Male image; (b) salt and pepper noise image with noise density of 50; (c) result of proposed algorithm; (d) result of WNNM algorithm; (e) result of WSNM algorithm; (f) result of BM3D algorithm; (g) result of RM algorithm

    Fig. 4. Comparison of denoising details of Male image in salt and pepper noise with the noise density of 50. Figs. 4(c)-(g) are detail parts of Fig. 4(a). (a) Original Male image; (b) salt and pepper noise image with noise density of 50; (c) result of proposed algorithm; (d) result of WNNM algorithm; (e) result of WSNM algorithm; (f) result of BM3D algorithm; (g) result of RM algorithm

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    • Table 1. Comparison between proposed algorithm and other algorithms on PSNR and FSIM by selecting four images of Boat, Male, Peppers, and Pentagon under different salt and pepper noise densities

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      Table 1. Comparison between proposed algorithm and other algorithms on PSNR and FSIM by selecting four images of Boat, Male, Peppers, and Pentagon under different salt and pepper noise densities

      pImageProposedWNNMRMWSNMBM3D
      20Boat32.590.95331.37/0.91331.23/0.90131.98/0.91231.88/0.921
      Male32.190.93931.58/0.91031.29/0.90731.45/0.92131.90/0.923
      Peppers32.230.90131.08/0.88931.25/0.89231.89/0. 91731.85/0. 922
      Pentagon31.950.92131.28/0.90131.75/0.86331.47/0.90231.90/0.910
      30Boat30.990.83328.98/0.82728.86/0.84229.19/0.84828.74/0.842
      Male30.650.89428.58/0.82127.96/0.82928.81/0.85928.52/0.831
      Peppers30.580.88728.71/0.84127.89/0.81928.76/0.81228.54/0.827
      Pentagon30.420.89328.63/0.83027.94/0.85328.68/0.80528.72/0.841
      40Boat28.220.82127.13/0.71226.31/0.70327.35/0.74527.10/0.727
      Male28.160.83726.78/0.70426.20/0.65127.46/0.76027.02/0.704
      Peppers28.440.80326.22/0.69226.16/0.60927.30/0.72526.52/0.687
      Pentagon28.680.81926.42/0.69926.05/0.60827.27/0.71326.32/0.647
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    Junrui Lü, Xuegang Luo, Shifeng Qi, Zhenming Peng. Image Denoising Using Weighted Nuclear Norm Minimization with Preserving Local Structure[J]. Laser & Optoelectronics Progress, 2019, 56(16): 161006

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    Paper Information

    Category: Image Processing

    Received: Feb. 18, 2019

    Accepted: Mar. 22, 2019

    Published Online: Aug. 5, 2019

    The Author Email: Xuegang Luo (543884841@qq.com)

    DOI:10.3788/LOP56.161006

    Topics

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