Adaptive Residual Channel Attention Network for Single Image Super-Resolution

Table 2

Quantitative PSNR/SSIM comparison for scaling factors ×2, ×3, and ×4, on testing benchmarks Set5, Set14, B100, Urban100, and Manga109. Our performance is shown in bold.

Scale	Method	Set5 [35] PSNR/SSIM	Set14 [36] PSNR/SSIM	B100 [37] PSNR/SSIM	Urban100 [38] PSNR/SSIM	Magan109 [39] PSNR/SSIM
×2	Bicubic	33.66/0.9299	30.24/0.8688	29.56/0.8431	26.88/0.8403	30.80/0.9339
	SRCNN [6]	36.66/0.9542	32.45/0.9067	31.36/0.8879	29.50/0.8946	35.60/0.9750
	VDSR [8]	37.53/0.9587	33.03/0.9124	31.90/0.8960	30.76/0.9140	37.22/0.9750
	FSRCNN [7]	37.00/0.9558	32.63/0.9088	31.53/0.8920	29.88/0.9020	36.67/0.9710
	DRCN [11]	37.63/0.9588	33.04/0.9118	31.85/0.8942	30.75/0.9133	37.55/0.9732
	LapSRN [10]	37.52/0.9591	32.99/0.9124	31.80/0.8952	30.41/0.9103	37.27/0.9740
	DRRN [12]	37.74/0.9591	33.23/0.9542	32.05/0.8973	31.23/0.9188	37.88/0.9749
	MemNet [40]	37.78/0.9597	33.28/0.9142	32.08/0.8978	31.31/0.9195	37.72/0.9542
	IDN [41]	37.83/0.9600	33.30/0.9148	32.08/0.8985	31.27/0.9196	38.01/0.9740
	EDSR (B) [9]	37.99/0.9604	33.57/0.9175	32.16/0.8994	31.92/0.9272	38.54/0.9749
	SRMDNF [42]	37.79/0.9601	33.32/0.9159	32.05/0.8985	31.33/0.9204	38.07/0.9769
	CARN [43]	37.72/0.9590	33.52/0.9166	36.66/0.8978	31.92/0.9256	38.36/0.9761
	Ms-LapSRN [44]	37.62/0.9600	33.13/0.9130	31.93/0.8970	30.82/0.0150	37.38/0.9765
	Ours	38.006/0.9605	33.54/0.9173	32.15/0.8992	32.13/0.9276	38.70/0.9750
×3	Bicubic	30.39/0.8682	27.55/0.7742	27.21/0.7385	24.46/0.7349	26.95/0.8556
	SRCNN [6]	32.75/0.9090	29.30/0.8215	28.41/0.7863	26.24/0.7989	30.48/0.9117
	FSRCNN [7]	33.18/0.9140	29.37/0.8240	28.53/0.7910	26.43/0.8080	31.10/0.9210
	VDSR [8]	33.66/0.9213	29.77/0.8314	28.82/0.7976	27.14/0.8279	32.01/0.9340
	DRCN [11]	33.82/0.9226	29.96/0.8311	28.80/0.7963	27.53/0.8276	32.66/0.9343
	LapSRN [10]	33.81/0.9220	29.79/0.8325	28.82/0.7980	27.07/0.8275	32.21/0.9350
	DRRN [12]	34.03/0.9244	29.99/0.8349	28.95/0.8004	27.53/0.8378	32.71/0.9379
	MemNet [40]	34.09/0.9248	30.00/0.8350	28.96/0.8001	27.56/0.8376	32.51/0.9369
	IDN [41]	34.11/0.9253	29.99/0.8354	28.95/0.8013	27.42/0.8359	32.71/0.9381
	EDSR (B) [9]	34.37/0.9270	30.28/0.8417	29.09/0.8052	28.15/0.8527	33.45/0.9439
	SRMDNF [42]	34.12/0.9542	30.04/0.8382	28.97/0.8025	27.57/0.8398	33.00/0.9403
	CARN [43]	34.29/0.9542	30.29/0.8407	29.06/0.8034	28.06/0.8493	33.50/0.9440
	Ms-LapSRN [44]	33.88/0.9230	29.89/0.8340	28.87/0.8000	27.23/0.8310	32.28/0.9360
	Ours	34.36/0.9542	30.30/0.8412	29.07/0.8045	28.14/0.8514	33.50/0.9439
×4	Bicubic	28.42/0.8104	26.00/0.7027	25.96/0.6675	23.14/0.6577	24.89/0.7866
	SRCNN [6]	30.48/0.8628	27.50/0.7513	26.90/0.7101	24.52/0.7221	27.58/0.8555
	FSRCNN [7]	30.72/0.8660	27.61/0.7550	26.98/0.7150	24.62/0.7280	27.90/0.8610
	VDSR [8]	31.35/0.8838	28.01/0.7674	27.29/0.7251	25.18/0.7524	28.83/0.8870
	DRCN [11]	31.53/0.8854	28.02/0.7670	27.23/0.7233	25.14/0.7510	28.93/0.8854
	LapSRN [10]	31.54/0.8842	28.09/0.7700	27.32/0.7275	25.21/0.7562	29.09/0.8900
	DRRN [12]	31.68/0.8888	28.21/0.7720	27.38/0.7284	25.44/0.7638	29.45/0.8946
	MemNet [40]	31.74/0.8893	38.26/0.7723	27.40/0.7281	25.50/0.7630	29.42/0.8942
	IDN [41]	31.82/0.8903	28.25/0.7730	27.41/0.7297	25.41/0.7632	29.41/0.8942
	EDSR (B) [9]	32.09/0.8938	28.58/0.7813	27.57/0.7357	26.04/0.7849	30.35/0.9067
	SRMDNF [42]	31.96/0.8925	28.35/0.7787	27.49/0.7337	25.68/0.7731	30.09/0.9024
	CARN [43]	32.13/0.8937	28.60/0.7806	27.58/0.7349	26.07/0.7837	30.47/0.9084
	Ms-LapSRN [44]	31.62/0.8870	28.16/0.7720	27.36/0.7290	25.32/0.7600	29.18/0.8920
	Ours	32.13/0.8941	28.58/0.7816	27.56/0.7356	26.09/0.7859	30.42/0.9074