Image Deraining Algorithm via Multiflow Expansion Residual Dense Network

Fig. 3. Examples of image decomposition. (a) Rainy image; (b) base layer of rainy image; (c) base layer of rainless image; (d) detail layer of rainy image

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Fig. 4. Color curves of base layer. (a) Rainless image; (b) rainy image

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Fig. 5. Example of residual image. (a) Rainless image; (b) rainy image; (c) residual image

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Fig. 6. Visual comparison of different algorithms on composite rainy images. (a1)(a2) Rainy image; (b1)(b2) GMM algorithm; (c1)(c2) DSC algorithm; (d1)(d2) DCN algorithm; (e1)(e2) DID-MDN algorithm; (f1)(f2) MERDN algorithm

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Fig. 7. Visual comparison of different algorithms on real rainy images. (a1)(a2) Rainy image; (b1)(b2) GMM algorithm; (c1)(c2) DSC algorithm; (d1)(d2) DCN algorithm; (e1)(e2) DID-MDN algorithm; (f1)(f2) MERDN algorithm

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Table 1. Detailed setting of parameter layer
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Table 1. Detailed setting of parameter layer
Layer Filter size Dilated factor Number of filters
Convolution×1 3×3 1 32
Convolution×1 3×3 2 32
Convolution×1 3×3 3 32
Dense block×9 1×1 1 64
Dense block×9 3×3 2 32
Convolution×1 3×3 1 3

Table 2. PSNR of different algorithms unit: dB
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Table 2. PSNR of different algorithms unit: dB
Algorithm Dataset 1 Dataset 2 Dataset 3
GMM 23.75 28.36 30.70
DSC 22.03 24.16 30.02
DCN 21.97 28.17 29.42
ID-CGAN 23.81 23.39 20.78
DID-MDN 26.17 28.27 28.90
MERDN 26.46 28.81 31.90

Table 3. SSIM of different algorithms
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Table 3. SSIM of different algorithms
Algorithm Dataset 1 Dataset 2 Dataset 3
GMM 0.7844 0.8712 0.8928
DSC 0.7985 0.8679 0.8679
DCN 0.8333 0.9134 0.9033
ID-CGAN 0.8072 0.8275 0.8519
DID-MDN 0.8409 0.8625 0.8762
MERDN 0.8841 0.9173 0.9381

Table 4. Efficiency comparison of different algorithms
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Table 4. Efficiency comparison of different algorithms
Algorithm GMM DCN DID-MDN MERDN
Time /s 897 29 11 5

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Weiwei Wang, Yayu Zhai, Ping Chen, Fengcai Cao. Image Deraining Algorithm via Multiflow Expansion Residual Dense Network[J]. Laser & Optoelectronics Progress, 2021, 58(4): 0410024

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