Underwater Image Enhancement Based on Pyramid Attention Mechanism and Generative Adversarial Network

Fig. 6. Qualitative comparison of different methods on test set A. (a) Underwater images; (b) GC; (c) UDCP; (d) LDCP; (e) UWCNN; (f) FUnIE-GAN; (g) proposed method; (h) reference

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Fig. 7. Qualitative comparison of different methods on test set B. (a) Underwater images; (b) GC; (c) UDCP; (d) LDCP; (e) UWCNN; (f) FUnIE-GAN; (g) proposed method

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Table 1. Experimental results obtained by GAN and FPAGAN on test set A
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Table 1. Experimental results obtained by GAN and FPAGAN on test set A
Method PSNR SSIM
GAN 21.9328 0.7330
FPAGAN 22.3984 0.7418

Table 2. Experimental results obtained by GAN and FPAGAN on test set B
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Table 2. Experimental results obtained by GAN and FPAGAN on test set B
Method UIQM IE NIQE
GAN 2.9086 4.6889 42.5059
FPAGAN 2.9457 4.6925 37.6927

Table 3. Quantitative comparison of different methods on test set A
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Table 3. Quantitative comparison of different methods on test set A
Metrics GC UDCP LDCP UWCNN FUnIE-GAN Ours
PSNR 15.1830 13.2231 13.9795 16.1344 19.3574 22.3984
SSIM 0.6495 0.5354 0.5422 0.6061 0.6923 0.7418

Table 4. Quantitative comparison of different methods on test set B

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Table 4. Quantitative comparison of different methods on test set B

Metrics	GC	UDCP	LDCP	UWCNN	FUnIE-GAN	Ours
UIQM	2.3361	1.6766	2.0934	2.2210	2.3418	2.9457
IE	4.2288	4.4678	4.5452	4.3699	4.6504	4.6925
NIQE	28.6067	31.0210	27.7121	39.8560	41.1683	37.6927
OG-IQA	-0.4489	-0.6760	-0.7238	-0.5479	-0.7475	-0.8001

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Yue Wang, Dexing Wang, Hongchun Yuan, Ruoyou Wu, Peng Gong. Underwater Image Enhancement Based on Pyramid Attention Mechanism and Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2021, 58(16): 1610006

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