Author Affiliations
College of Mathematics and Information Science, Xianyang Normal University, Xianyang, Shaanxi 712000, Chinashow less
Fig. 1. Schematic of atmospheric scattering model
Fig. 2. Proposed network structure
Fig. 3. Flow chart of 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
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
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
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
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
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
Fig. 10. Comparison results of different algorithms. (a) Average gradient; (b) information entropy
Filter size | Pad | Stride |
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1×1×16 | 0 | 1 | 3×3×165×5×167×7×16 | 123 | 111 |
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Table 1. Multi-scale convolution parameters
Indicator | DCP | BCCR | SVDSR | CAP | MSCNN | Proposed |
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RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB ↑ | 0.02730.61460.57630.756311.004417.026515.8246 | 0.01330.56781.13160.24328.806913.351312.5962 | 0.06290.62450.36430.75019.831814.628015.2510 | 0.02590.61791.63660.79087.149916.799519.8702 | 0.02580.60351.20880.69667.979916.385520.6022 | 0.02460.63271.25290.844111.195617.883123.4425 | SSIM ↑ | 0.7782 | 0.6097 | 0.7572 | 0.8769 | 0.8797 | 0.9524 |
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Table 2. Evaluation indicators of defogging results of image Teddy by different algorithms
Indicator | DCP | BCCR | SVDSR | CAP | MSCNN | Proposed |
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RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB↑ | 0.03200.59470.23000.93046.274614.980111.4845 | 0.02270.64402.54090.34556.956013.321810.6521 | 0.07560.67240.25360.76947.469613.741719.4985 | 0.03130.61591.37980.52363.949414.551824.6558 | 0.02970.59550.68060.55774.367114.313222.3259 | 0.02990.67812.58200.98437.556216.790824.7741 | SSIM ↑ | 0.8412 | 0.6339 | 0.8601 | 0.8769 | 0.8583 | 0.9245 |
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Table 3. Evaluation indicators of defogging results of image Dolls by different algorithms
Indicator | DCP | BCCR | SVDSR | CAP | MSCNN | Proposed |
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RMSE ↓UQI ↑Cross entropy ↑Tone reduction↑Average gradient ↑Entropy ↑PSNR /dB↑ | 0.03750.83781.01920.745916.562913.314924.2385 | 0.02870.92201.43420.624322.515215.109816.2698 | 0.09660.88381.02650.63237.326912.659815.2502 | 0.02310.52310.22600.80455.614615.540523.4958 | 0.02410.68950.48520.65565.231214.231921.2102 | 0.02250.98670.04210.965022.768016.699527.3441 | SSIM ↑ | 0.8567 | 0.7357 | 0.7279 | 0.9462 | 0.8975 | 0.9690 |
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Table 4. Evaluation indicators of defogging results of image Cloth by different algorithms