Image Dehazing Algorithm Based on Multi-Scale Fusion and Adversarial Training

Fig. 5. Dehazing results of the synthetic hazy images. (a) Hazy image; (b) DCP; (c) FVR; (d) BCCR; (e) GRM; (f) CAP; (g) NLD; (h) DehazeNet; (i) MSCNN; (j) AOD-Net; (k) proposed method; (l) haze-free image

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Fig. 6. Dehazing results of the real-world hazy images. (a) Hazy image; (b) DCP; (c) FVR; (d) BCCR; (e) GRM; (f) CAP; (g) NLD; (h) DehazeNet; (i) MSCNN; (j) AOD-Net; (k) proposed method

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Table 1. Comparison of full-reference quality metrics tested on synthetic hazy images
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Table 1. Comparison of full-reference quality metrics tested on synthetic hazy images
Quality metric DCP FVR BCCR GRM CAP NLD DehazeNet MSCNN AOD-Net Proposed
PSNR/dB 16.62 15.72 16.88 18.86 19.05 17.29 21.14 17.57 19.06 25.08
SSIM 0.8179 0.7483 0.7913 0.8553 0.8364 0.7489 0.8472 0.8102 0.8504 0.9468

Table 2. Comparison of no-reference quality metrics tested on synthetic hazy images
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Table 2. Comparison of no-reference quality metrics tested on synthetic hazy images
Quality metric DCP FVR BCCR GRM CAP NLD DehazeNet MSCNN AOD-Net Proposed
SSEQ 64.94 67.75 65.83 63.30 64.69 67.46 65.46 65.31 67.65 68.07
BLIINDS-II 74.41 75.63 74.45 73.46 73.41 74.85 71.71 74.34 79.02 81.96

Table 3. Comparison of full-reference quality metrics tested on synthetic hazy images with different haze concentration levels

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Table 3. Comparison of full-reference quality metrics tested on synthetic hazy images with different haze concentration levels

Hazedensity	Qualitymetric	DCP	FVR	BCCR	GRM	CAP	NLD	Dehaze-Net	MSCNN	AOD-Net	Ourproposed
Light hazeβ∈[0.6,0.9]	PSNR /dB	16.10	17.18	16.91	18.64	20.88	17.52	24.24	19.72	22.40	26.11
Light hazeβ∈[0.6,0.9]	SSIM	0.8158	0.7682	0.7978	0.8528	0.8597	0.7558	0.9044	0.8489	0.8980	0.9579
Medium hazeβ∈[1.0,1.4]	PSNR /dB	16.58	16.00	17.07	18.74	19.68	17.37	22.02	17.25	19.61	24.95
Medium hazeβ∈[1.0,1.4]	SSIM	0.8210	0.7538	0.7942	0.8576	0.8450	0.7487	0.8870	0.8110	0.8616	0.9472
Heavy hazeβ∈[1.5,1.8]	PSNR /dB	17.15	14.42	17.14	19.11	17.21	17.06	18.67	15.10	16.16	24.40
Heavy hazeβ∈[1.5,1.8]	SSIM	0.8259	0.7289	0.7906	0.8555	0.8120	0.7438	0.8454	0.7723	0.8064	0.9381

Table 4. Comparison of no-reference quality metrics tested on real-world hazy images
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Table 4. Comparison of no-reference quality metrics tested on real-world hazy images
Quality metric DCP FVR BCCR GRM CAP NLD DehazeNet MSCNN AOD-Net Proposed
SSEQ 68.65 67.75 66.63 70.19 67.67 67.96 68.34 68.44 70.05 69.99
BLIINDS-II 69.35 72.10 68.55 79.60 63.55 70.80 60.35 62.65 74.75 82.25

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Yuhang Liu, Shuai Wu. Image Dehazing Algorithm Based on Multi-Scale Fusion and Adversarial Training[J]. Laser & Optoelectronics Progress, 2020, 57(6): 061015

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Paper Information

Category: Image Processing

Received: Jul. 30, 2019

Accepted: Sep. 2, 2019

Published Online: Mar. 6, 2020

The Author Email: Yuhang Liu (lyhang95@163.com), Shuai Wu (iswus@outlook.com)

DOI:10.3788/LOP57.061015

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Comparison of full-reference quality metrics tested on synthetic hazy images

Table 1. Comparison of full-reference quality metrics tested on synthetic hazy images

Table 2. Comparison of no-reference quality metrics tested on synthetic hazy images

Table 2. Comparison of no-reference quality metrics tested on synthetic hazy images

Table 3. Comparison of full-reference quality metrics tested on synthetic hazy images with different haze concentration levels

Table 3. Comparison of full-reference quality metrics tested on synthetic hazy images with different haze concentration levels

Table 4. Comparison of no-reference quality metrics tested on real-world hazy images

Table 4. Comparison of no-reference quality metrics tested on real-world hazy images