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Acta Photonica Sinica, Volume. 51, Issue 11, 1110003(2022)

Remote Sensing Image Denoising Algorithm with Multi-receptive Field Feature Fusion and Enhancement

Xueyuan GUAN... Wei HU* and Heng FU |Show fewer author(s)
Author Affiliations
  • State Key Laboratory of Transient Physics，Nanjing University of Science and Technology，Nanjing 210094，China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (16)
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    References (25)
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    Figures & Tables(16)
    MRFENet structure diagram

    Fig. 1. MRFENet structure diagram

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    Structure diagram of global feature extraction module

    Fig. 2. Structure diagram of global feature extraction module

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    Examples of NWPU-RESISC45 dataset

    Fig. 3. Examples of NWPU-RESISC45 dataset

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    Example of different noise intensity datasets

    Fig. 4. Example of different noise intensity datasets

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    Example of RSSCN7 datasets and real noise datasets

    Fig. 5. Example of RSSCN7 datasets and real noise datasets

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    Performance curves under different parameters

    Fig. 6. Performance curves under different parameters

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    Loss curve chart

    Fig. 7. Loss curve chart

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    Example of denoising results of different algorithms（σ=15）

    Fig. 8. Example of denoising results of different algorithms（ σ=15）

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    Example of denoising results of different algorithms（σ=20）

    Fig. 9. Example of denoising results of different algorithms（σ=20）

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    Example of denoising results of different algorithms（σ=35）

    Fig. 10. Example of denoising results of different algorithms（σ=35）

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    Example of denoising results of different algorithms（σ=50）

    Fig. 11. Example of denoising results of different algorithms（σ=50）

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    Denoising results of various methods under different noise intensity

    Fig. 12. Denoising results of various methods under different noise intensity

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    Example of real noise denoising result

    Fig. 13. Example of real noise denoising result

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    • Table 1. Quantitative results of different noise intensities under each method

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      Table 1. Quantitative results of different noise intensities under each method

      DatasetMethodsPSNR（dB）/SSIM σ2 = 15PSNR（dB）/SSIM σ2 = 20PSNR（dB）/SSIM σ2 = 35PSNR（dB）/SSIM σ2 = 50PSNR（dB）/SSIM Hybrid noise
      NWPU-RESISC45NLM29.315/0.863 725.469/0.811 722.187/0.723 120.142/0.625 724.012/0.752 3
      BM3D29.588/0.878 026.352/0.832 524.732/0.769 522.025/0.710 525.131/0.793 1
      DnCNN30.732/0.924 128.565/0.850 327.182/0.833 725.149/0.789 727.973/0.839 0
      RIDNet30.893/0.924 529.438/0.875 127.679/0.841 525.493/0.796 328.512/0.852 6
      REDJ31.096/0.925 630.635/0.891 728.031/0.849 925.592/0.805 129.467/0.885 2
      MRFENet（ours）31.478/0.944 730.833/0.894 528.622/0.854 726.036/0.812 729.499/0.890 3
      RSSCN7NLM28.078/0.879 825.377/0.789 524.165/0.721 520.575/0.601 924.537/0.761 7
      BM3D28.842/0.886 826.174/0.812 424.691/0.760 321.362/0.673 525.882/0.790 1
      DnCNN31.949/0.929 728.051/0.843 726.322/0.839 225.033/0.819 327.256/0.840 3
      RIDNet32.340/0.943 428.866/0.889 328.149/0.847 225.474/0.822 928.481/0.867 4
      REDJ32.290/0.950 829.472/0.891 028.228/0.858 326.163/0.830 529.022/0.877 1
      MRFENet（ours）32.434/0.958 729.636/0.892 628.386/0.862 726.635/0.849 829.255/0.832 5

    • Table 2. Quantitative results of real noise images under each method

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      Table 2. Quantitative results of real noise images under each method

      DatasetEvaluationNoisyNLMBM3DDnCNNRIDNetREDJMRFENet（ours）

      Washington DC

      mall

      		NIQE↓7.978 78.439 39.595 19.405 610.329 412.738 58.138 5
      BRISQUE↓43.804 850.605 843.456 343.457 843.455 543.458 442.458 2
      PIQE↓39.366 083.656 448.993 338.598 640.470 745.659 737.056 6

    • Table 3. Quantitative results of ablation experiments

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      Table 3. Quantitative results of ablation experiments

      ModuleGFECAMStep feature fusionPSNR（dB）/SSIM
      Experiment√××28.563/0.870 2
      ×√×28.427/0.863 7
      ××√28.556/0.869 3
      √√×29.355/0.881 4
      √×√29.165/0.873 7
      ×√√29.545/0.882 1
      √√√29.636/0.892 6
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    Xueyuan GUAN, Wei HU, Heng FU. Remote Sensing Image Denoising Algorithm with Multi-receptive Field Feature Fusion and Enhancement[J]. Acta Photonica Sinica, 2022, 51(11): 1110003

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    Paper Information

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    Received: Jul. 6, 2022

    Accepted: Aug. 15, 2022

    Published Online: Dec. 13, 2022

    The Author Email: HU Wei (519351243@qq.com)

    DOI:10.3788/gzxb20225111.1110003

    Topics

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