Super-Resolution Image Reconstruction Based on Residual Channel Attention and Multilevel Feature Fusion

Zhihong Xi; Kunpeng Yuan

doi:10.3788/LOP57.041504

Laser & Optoelectronics Progress, Volume. 57, Issue 4, 041504(2020)

Super-Resolution Image Reconstruction Based on Residual Channel Attention and Multilevel Feature Fusion

Zhihong Xi^* and Kunpeng Yuan

Author Affiliations

College of Information and Communication Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China

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Abstract Get PDF(in Chinese)

Figures & Tables(12)

Fig. 1. Structure of ESPCN network

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Fig. 2. Structure of VDSR network

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Fig. 3. Network structure of proposed algorithm

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Fig. 4. Recursive unit module. (a) Recursive unit module composition; (b) residual channel attention; (c) multilevel feature fusion

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Fig. 5. Variation of mean PSNR with the number of iterations for different layers at Set5 test set

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Fig. 6. Relationship between number of parameters of different network structures and mean PSNR at Set5 test set

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Fig. 7. Relationship between run time of different methods and mean PSNR at Set5 test set

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Fig. 8. Comparison of zebra image recovered with different algorithms

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Fig. 9. Comparison of ppt image recovered with different algorithms

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Table 1. Means PSNR of different RCAF model components at Set 5 test set
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Table 1. Means PSNR of different RCAF model components at Set 5 test set
Multilevelfeature fusion Residualchannel attention PSNR
√ √ 31.61
× √ 31.35
√ × 31.40
× × 30.94

Table 2. Mean PSNR of different algorithms at Set5, Set14, and BSD100 test sets

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Table 2. Mean PSNR of different algorithms at Set5, Set14, and BSD100 test sets

Test set	Scale	Bicubic	SRCNN^[8]	ESPCN^[10]	FSRCNN^[9]	VDSR^[11]	RCAF
	2	33.68	36.19	36.38	36.45	37.34	37.62
Set5	3	30.45	32.46	32.71	32.59	33.47	34.00
	4	28.46	30.15	30.29	30.42	30.78	31.61
	2	30.21	32.10	32.20	32.21	32.82	33.24
Set14	3	27.51	28.99	29.12	29.12	29.51	29.87
	4	25.98	27.23	27.17	27.43	27.62	28.11
	2	29.43	30.88	30.93	31.24	31.51	31.81
BSD100	3	27.08	28.06	28.16	28.25	28.43	28.72
	4	25.84	26.63	26.59	26.85	26.87	27.18

Table 3. Mean SSIM of different algorithms at test sets Set5, Set14, and BSD100

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Table 3. Mean SSIM of different algorithms at test sets Set5, Set14, and BSD100

Dataset	Scale	Bicubic	SRCNN^[8]	ESPCN^[10]	FSRCNN^[9]	VDSR^[11]	RCAF
	2	0.931	0.955	0.957	0.957	0.958	0.963
Set5	3	0.869	0.911	0.915	0.912	0.919	0.930
	4	0.810	0.862	0.863	0.866	0.875	0.893
	2	0.869	0.958	0.960	0.963	0.910	0.964
Set14	3	0.774	0.884	0.887	0.892	0.827	0.897
	4	0.702	0.821	0.823	0.827	0.759	0.842
	2	0.844	0.880	0.883	0.887	0.892	0.897
BSD100	3	0.740	0.776	0.781	0.780	0.793	0.797
	4	0.670	0.692	0.694	0.701	0.719	0.720

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Zhihong Xi, Kunpeng Yuan. Super-Resolution Image Reconstruction Based on Residual Channel Attention and Multilevel Feature Fusion[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041504

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