A generative adversarial network incorporating dark channel prior loss used for single image defogging

Deqiang Cheng; Yangyang You; Qiqi Kou; Jinyang Xu

doi:10.12086/oee.2022.210448

Opto-Electronic Engineering, Volume. 49, Issue 7, 210448(2022)

A generative adversarial network incorporating dark channel prior loss used for single image defogging

Deqiang Cheng1...2, Yangyang You2, Qiqi Kou3,* and Jinyang Xu2 |Show fewer author(s)

¹Engineering Research Center of Underground Space Intelligent Control, Ministry of Education, Xuzhou, Jiangsu 221000, China

²School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, Jiangsu 221000, China

³School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221000, China

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Figures & Tables(15)

Fig. 1. Framework of adversarial generation network

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Fig. 2. Dark channel feature comparison.(a) Original images; (b) Dark channel feature

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Fig. 3. Dark channel feature intensity distribution. (a) Intensity distribution; (b) Average intensity distribution of 5000 images

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Fig. 4. Framework of the proposed algorithm

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Fig. 5. Qualitative comparison on synthetic images

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Fig. 6. Qualitative comparison on real hazy images

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Fig. 7. Quantitative comparison with control group on SOTS test-set & synthetic images of HSTS test-set

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Fig. 8. Qualitative comparison with control groups on real images of HSTS test-set

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Fig. 9. Quantitative comparison with control group on real hazy images of HSTS test-set

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Table 1. Parameters of generator network
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Table 1. Parameters of generator network
卷积层 Conv Res Conv Res Conv Res Upconv Res Upconv Res Conv Tanh
输入通道数 3 64 64 128 128 256 256 128 128 64 64 3
输出通道数 64 64 128 128 256 256 128 128 64 64 3 3
卷积核尺寸 7 5 3 4 4 7
步长 1 2 2 2 2 1
边界填充 3 1 1 1 1 3

Table 2. Parameters of the discriminator network
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Table 2. Parameters of the discriminator network
卷积层 Conv1 Conv2 Conv3 Conv4 Conv5
输出通道数 64 128 256 512 1
卷积核大小 4 4 4 4 4
步长 2 2 2 1 1

Table 3. Quantitative results of each algorithm on SOTS test-set & synthetic images of HSTS test-set

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Table 3. Quantitative results of each algorithm on SOTS test-set & synthetic images of HSTS test-set

数据集	HSTS		SOTS-outdoor		SOTS-indoor
评价指标	PSNR	SSIM	PSNR	SSIM	PSNR	SSIM
DCP^[7]	17.22	0.80	17.56	0.82	20.15	0.87
BCCR^[9]	15.09	0.74	15.49	0.78	16.88	0.79
CAP^[10]	21.54	0.87	22.30	0.91	19.05	0.84
MSCNN^[15]	18.29	0.84	19.56	0.86	17.11	0.81
D-Net^[16]	24.49	0.92	22.72	0.86	21.14	0.85
AOD-Net^[17]	21.58	0.92	21.34	0.92	19.38	0.85
GFN^[18]	22.94	0.87	21.49	0.84	22.32	0.88
DEnergy^[26]	24.44	0.93	24.08	0.93	19.25	0.83
本文算法	25.35	0.96	25.17	0.96	23.70	0.82

Table 4. Quantitative results of each algorithm on D-HAZY & HazeRD & BeDDE test-set

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Table 4. Quantitative results of each algorithm on D-HAZY & HazeRD & BeDDE test-set

数据集	D-HAZY		HazeRD		BeDDE
评价指标	PSNR	SSIM	PSNR	SSIM	VSI	VI	RI
DCP^[7]	15.09	0.83	14.01	0.39	0.946	0.911	0.965
MSCNN^[15]	13.57	0.80	15.58	0.42	0.947	0.892	0.972
D-Net^[16]	13.76	0.81	15.53	0.41	0.952	0.890	0.972
AOD-Net^[17]	13.13	0.79	15.63	0.45	0.954	0.896	0.970
CycleGAN^[22]	13.55	0.77	15.64	0.44	0.942	0.866	0.961
RefineDNet^[39]	15.44	0.83	15.61	0.43	0.960	0.907	0.971
SM-Net^[24]	15.32	0.81	15.55	0.40	0.961	0.899	0.969
本文算法	15.39	0.82	15.59	0.44	0.967	0.899	0.967

Table 5. Quantitative results of the control groups & proposed algorithm on real images of HSTS test-set

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Table 5. Quantitative results of the control groups & proposed algorithm on real images of HSTS test-set

		Pic1	Pic2	Pic3	Pic4	Pic5	Pic6	Pic7	Pic8	Pic9	Pic10
对照组1	e	0.0162	-0.0506	0.0868	0.1629	0.3869	0.3391	0.6262	1.0014	0.9579	0.0160
	r	1.0100	1.0435	1.1291	1.0450	1.6743	1.4286	1.4641	1.4606	1.8278	1.0928
	p	0.0000	0.0000	0	0.0019	0	0.0002	0	0.0002	0.0003	0.0000
对照组2	e	0.3360	0.6421	0.2709	0.0774	0.3833	0.4338	0.7845	1.8348	0.6207	0.4650
	r	1.4605	1.4222	1.2640	1.2047	1.6926	1.4382	1.3379	1.5126	1.6021	1.1351
	p	0.0102	0.1155	0.0001	0.0081	0	0.0072	0.0002	0.0081	0.0016	0.0286
本文算法	e	0.3534	0.6061	0.8870	0.1232	0.4504	0.6265	1.2378	2.0714	1.1443	0.5197
	r	1.6284	1.5576	1.6631	1.3572	2.1599	1.6863	1.5793	1.7814	1.9561	1.3026
	p	0.0082	0.1015	0.0001	0.0020	0	0.0079	0.0011	0.0108	0.0013	0.0153

Table 6. Run time of each algorithm on SOTS test-set
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Table 6. Run time of each algorithm on SOTS test-set
DCP^[7] CAP^[10] MSCNN^[15] D-Net^[16] AOD-Net^[17] GFN^[18] CycleGAN^[22] 本文算法
Device CPU CPU GPU CPU GPU GPU GPU GPU
Run time 1.74 0.72 3.41 3.33 0.56 6.10 2.96 1.37

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Deqiang Cheng, Yangyang You, Qiqi Kou, Jinyang Xu. A generative adversarial network incorporating dark channel prior loss used for single image defogging[J]. Opto-Electronic Engineering, 2022, 49(7): 210448

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Paper Information

Category: Article

Received: Jan. 19, 2022

Accepted: --

Published Online: Aug. 1, 2022

The Author Email: Kou Qiqi (1120074179@qq.com)

DOI:10.12086/oee.2022.210448

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Parameters of generator network

Table 1. Parameters of generator network

Table 2. Parameters of the discriminator network

Table 2. Parameters of the discriminator network

Table 3. Quantitative results of each algorithm on SOTS test-set & synthetic images of HSTS test-set

Table 3. Quantitative results of each algorithm on SOTS test-set & synthetic images of HSTS test-set

Table 4. Quantitative results of each algorithm on D-HAZY & HazeRD & BeDDE test-set

Table 4. Quantitative results of each algorithm on D-HAZY & HazeRD & BeDDE test-set

Table 5. Quantitative results of the control groups & proposed algorithm on real images of HSTS test-set

Table 5. Quantitative results of the control groups & proposed algorithm on real images of HSTS test-set

Table 6. Run time of each algorithm on SOTS test-set

Table 6. Run time of each algorithm on SOTS test-set

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