• Laser & Optoelectronics Progress
  • Vol. 56, Issue 11, 111005 (2019)
Jiantang Zhao*
Author Affiliations
  • College of Mathematics and Information Science, Xianyang Normal University, Xianyang, Shaanxi 712000, China
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    DOI: 10.3788/LOP56.111005 Cite this Article Set citation alerts
    Jiantang Zhao. Single-Image Defogging Algorithm Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111005 Copy Citation Text show less
    Schematic of atmospheric scattering model
    Fig. 1. Schematic of atmospheric scattering model
    Proposed network structure
    Fig. 2. Proposed network structure
    Flow chart of proposed algorithm
    Fig. 3. Flow chart of proposed algorithm
    Defogging results of foggy image Teddy by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Fig. 4. Defogging results of foggy image Teddy by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Defogging results of foggy image Dolls by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Fig. 5. Defogging results of foggy image Dolls by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Defogging results of foggy image Cloth by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Fig. 6. Defogging results of foggy image Cloth by different algorithms. (a) Foggy image; (b) original clear image; (c) DCP algorithm; (d) BCCR algorithm; (e) SVDSR algorithm; (f) CAP algorithm; (g) MSCNN algorithm; (h) proposed algorithm
    Comparison of defogging results of natural foggy image House. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Fig. 7. Comparison of defogging results of natural foggy image House. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Comparison of defogging results of natural foggy image Pumpkin. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Fig. 8. Comparison of defogging results of natural foggy image Pumpkin. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Comparison of defogging results of natural foggy image Girls. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Fig. 9. Comparison of defogging results of natural foggy image Girls. (a) Foggy image; (b) DCP algorithm; (c) BCCR algorithm; (d) SVDSR algorithm; (e) CAP algorithm; (f) MSCNN algorithm; (g) proposed algorithm
    Comparison results of different algorithms. (a) Average gradient; (b) information entropy
    Fig. 10. Comparison results of different algorithms. (a) Average gradient; (b) information entropy
    Filter sizePadStride
    1×1×1601
    3×3×165×5×167×7×16123111
    Table 1. Multi-scale convolution parameters
    IndicatorDCPBCCRSVDSRCAPMSCNNProposed
    RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB ↑0.02730.61460.57630.756311.004417.026515.82460.01330.56781.13160.24328.806913.351312.59620.06290.62450.36430.75019.831814.628015.25100.02590.61791.63660.79087.149916.799519.87020.02580.60351.20880.69667.979916.385520.60220.02460.63271.25290.844111.195617.883123.4425
    SSIM ↑0.77820.60970.75720.87690.87970.9524
    Table 2. Evaluation indicators of defogging results of image Teddy by different algorithms
    IndicatorDCPBCCRSVDSRCAPMSCNNProposed
    RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB↑0.03200.59470.23000.93046.274614.980111.48450.02270.64402.54090.34556.956013.321810.65210.07560.67240.25360.76947.469613.741719.49850.03130.61591.37980.52363.949414.551824.65580.02970.59550.68060.55774.367114.313222.32590.02990.67812.58200.98437.556216.790824.7741
    SSIM ↑0.84120.63390.86010.87690.85830.9245
    Table 3. Evaluation indicators of defogging results of image Dolls by different algorithms
    IndicatorDCPBCCRSVDSRCAPMSCNNProposed
    RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB↑0.03750.83781.01920.745916.562913.314924.23850.02870.92201.43420.624322.515215.109816.26980.09660.88381.02650.63237.326912.659815.25020.02310.52310.22600.80455.614615.540523.49580.02410.68950.48520.65565.231214.231921.21020.02250.98670.04210.965022.768016.699527.3441
    SSIM ↑0.85670.73570.72790.94620.89750.9690
    Table 4. Evaluation indicators of defogging results of image Cloth by different algorithms
    Jiantang Zhao. Single-Image Defogging Algorithm Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2019, 56(11): 111005
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