Acta Optica Sinica, Volume. 39, Issue 10, 1010001(2019)
Single Image Dehazing Method Based on Multi-Scale Convolution Neural Network
Figures & Tables(11)
Fig. 3. Comparison of activation functions. (a) ReLU activation function; (b) PReLU activation function
Fig. 6. Experimental results of synthesizing hazy images. (a) Hazy image; (b) standard haze-free image; (c) method in Ref. [7]; (d) method in Ref. [11]; (e) method in Ref. [12]; (f) method in Ref. [13]; (g) method in Ref. [14]; (h) proposed method
Fig. 7. Experimental results of real outdoor hazy images. (a) Hazy images; (b) method in Ref.[7]; (c) method in Ref.[11]; (d) method in Ref.[12]; (e) method in Ref.[13]; (f) method in Ref.[14]; (e) proposed method
Table 1. Parameter table of multi-scale feature extraction kernel
View tableTable 1. Parameter table of multi-scale feature extraction kernel
Type Conv Filter size 3×3 5×5 7×7 Filter number 5 5 5 Pad 0 0 0 Stride 1 1 1
Table 2. Analysis of experimental data of synthetic hazy images
View tableTable 2. Analysis of experimental data of synthetic hazy images
Image No. Method in Ref.[7] Method in Ref.[11] Method in Ref.[12] Method in Ref.[13] Method in Ref.[14] Proposed method PSNR / dB SSIM / % PSNR / dB SSIM / % PSNR / dB SSIM / % PSNR / dB SSIM / % PSNR / dB SSIM / % PSNR / dB SSIM / % 1 23.5357 85.26 17.5424 59.51 20.1796 72.37 26.1247 85.33 22.9888 79.48 28.8218 86.41 2 19.3044 80.04 17.7552 72.36 21.6655 84.07 22.1977 88.70 20.7442 87.39 23.6027 91.66 3 17.8051 79.60 16.9521 72.87 20.3959 81.23 22.7414 89.35 19.0486 84.07 26.0691 89.73 4 20.1277 82.78 19.0534 79.38 21.5623 84.42 21.8444 86.42 19.4202 81.68 24.3402 91.60 5 20.2825 81.85 21.1444 82.85 17.8848 79.29 27.6081 93.54 25.4410 90.61 29.1285 94.78
Table 3. Analysis of experimental data of outdoor hazy images
View tableTable 3. Analysis of experimental data of outdoor hazy images
Image No. Method in Ref.[7] Method in Ref.[11] Method in Ref.[12] Method in Ref.[13] Method in Ref.[14] Proposed method IE AG IE AG IE AG IE AG IE AG IE AG 1 7.0555 14.64 7.0652 18.34 7.3984 18.52 7.2445 17.22 7.4048 20.26 7.6821 23.32 2 7.5155 14.68 7.3049 17.04 7.8192 18.83 7.4186 13.93 7.6656 17.02 7.9266 18.99 3 7.3427 8.48 7.4737 10.89 7.8771 11.78 7.7043 8.62 7.6120 9.21 7.8935 12.08 4 7.5688 9.18 7.4250 11.93 7.8515 13.23 7.7608 9.62 7.7786 10.57 7.9815 14.31 5 7.2538 14.91 7.7213 18.17 7.3410 17.93 7.1746 10.43 7.3776 13.62 7.8690 19.41 6 7.1667 8.92 7.8371 10.40 7.7168 10.18 7.0263 7.85 7.2862 8.76 7.8937 10.47 7 7.2625 10.94 7.1136 12.96 7.6834 13.71 7.3200 8.67 7.5189 9.59 7.7274 14.06 8 6.2721 5.88 7.1459 7.32 7.3561 7.55 6.8363 5.97 6.9818 6.39 7.4534 7.57
Table 4. Running time of different algorithms for experimental imagess
View tableTable 4. Running time of different algorithms for experimental imagess
Method Experiment Indoor Outdoot Method in Ref.[7] 6.87 6.89 Method in Ref.[11] 3.41 3.65 Method in Ref.[12] 1.96 2.08 Method in Ref.[13] 1.21 1.26 Method in Ref.[14] 1.58 1.92 Proposed method 1.09 1.18
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Yong Chen, Hongguang Guo, Yapeng Ai. Single Image Dehazing Method Based on Multi-Scale Convolution Neural Network[J]. Acta Optica Sinica, 2019, 39(10): 1010001
Paper Information
Category: Image Processing
Received: Apr. 28, 2019
Accepted: Jun. 3, 2019
Published Online: Oct. 9, 2019
The Author Email: Chen Yong (edukeylab@126.com)
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