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Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1610006(2023)

Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm

Haichen Wang1, Shengqi Wang1, and Xueyou Hu2、*
Author Affiliations
  • 1College of Energy Materials and Chemical Engineering, Hefei University, Hefei 230601, Anhui, China
  • 2School of Advanced Manufacturing Engineering, Hefei University, Hefei 230601, Anhui, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (11)
    Equations (29)
    References (23)
    Cited By (1)
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    Figures & Tables(11)
    SSIM under different trancated values t

    Fig. 1. SSIM under different trancated values t

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    PSNR under different truncated values t

    Fig. 2. PSNR under different truncated values t

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    SSIM under different expected rank r

    Fig. 3. SSIM under different expected rank r

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    PSNR under different expected rank r

    Fig. 4. PSNR under different expected rank r

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    SSIM of the algorithm before and after improvement

    Fig. 5. SSIM of the algorithm before and after improvement

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    PSNR of the algorithm before and after improvement

    Fig. 6. PSNR of the algorithm before and after improvement

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    Denoising results of each denoising method in band 2 of Pavia University dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA

    Fig. 7. Denoising results of each denoising method in band 2 of Pavia University dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA

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    Denoising results of each denoising method in band 110 of Salinas dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA

    Fig. 8. Denoising results of each denoising method in band 110 of Salinas dataset. (a) Original image; (b) TNN-LLRGTV; (c) LLRGTV; (d) LRTDTV; (e) LRMR; (f) NAILRMA

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    Comparison of spectral curves of different denoising algorithms

    Fig. 9. Comparison of spectral curves of different denoising algorithms

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    • Table 1. Denoising result for mixed noise with equal intensity

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      Table 1. Denoising result for mixed noise with equal intensity

      Noise intensityParameterNAILRMALRMRLRTDTVLLRGTVTNN-LLRGTV

      G=0.04，

      S=0.10

      		SSIM0.60410.66020.65120.78090.8445
      PSNR /dB22.379026.597922.714629.770230.7610

      G=0.08，

      S=0.15

      		SSIM0.46270.52250.53130.67400.7706
      PSNR /dB18.671523.709618.960327.650528.7855

      G=0.12，

      S=0.20

      		SSIM0.36520.41710.43990.57530.7051
      PSNR /dB16.354421.524316.565925.320826.4233

    • Table 2. Denoising results for single-type noise

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      Table 2. Denoising results for single-type noise

      Noise intensityParameterNAILRMALRMRLRTDTVLLRGTVTNN-LLRGTV
      G=0.04SSIM0.76720.70210.76700.86610.8836
      PSNR /dB27.299227.710827.129027.548527.4349
      G=0.08SSIM0.65780.59630.68110.71590.7993
      PSNR /dB23.493525.511523.554928.340129.3462
      G=0.12SSIM0.58230.52740.62320.65750.7554
      PSNR /dB21.273924.256821.410327.224828.2942
      S=0.10SSIM0.76480.93690.77420.95660.9569
      PSNR /dB26.993136.720627.060137.268837.3276
      S=0.15SSIM0.68740.91350.71130.95580.9566
      PSNR /dB24.231135.061924.503337.093837.2341
      S=0.20SSIM0.61840.88800.65180.95500.9561
      PSNR /dB22.124833.623122.448136.916337.1171
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    Haichen Wang, Shengqi Wang, Xueyou Hu. Optimization of Hyperspectral Image Denoising Based on Local Truncated Nuclear Norm[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1610006

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    Paper Information

    Category: Image Processing

    Received: Aug. 12, 2022

    Accepted: Oct. 13, 2022

    Published Online: Aug. 15, 2023

    The Author Email: Xueyou Hu (xueyouhu@hfuu.edu.cn)

    DOI:10.3788/LOP222268

    Topics

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