Multi-scale dense feature fusion network for image super-resolution

Model	层次特征融合	密集特征融合	PSNR/dB	SSIM
NSFB	√	×	32.30	0.929 9
	×	√	32.35	0.930 5
Resblock	√	×	31.50	0.922 0
	×	√	31.53	0.922 2
RDN	√	×	32.22	0.928 9
	×	√	32.29	0.929 6
MSRB	√	×	32.29	0.929 7
	×	√	32.36	0.930 4
MFRB	√	×	32.35	0.930 3
	×	√	32.41	0.932 2

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

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Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

模型	缩放因子	Set5		Set14		BSD100		Urban100
模型	缩放因子	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
Bicubic	×2	33.66	0.929 9	30.24	0.868 7	29.56	0.843 1	26.88	0.840 1
SRCNN	×2	36.66	0.954 2	32.43	0.906 3	31.36	0.887 9	29.50	0.894 6
VDSR	×2	37.54	0.958 7	33.03	0.912 4	31.90	0.896 0	30.76	0.914 0
DRCN	×2	37.63	0.958 4	33.06	0.910 8	31.85	0.894 7	30.76	0.914 7
LapSRN	×2	37.53	0.959 1	33.08	0.910 9	31.80	0.894 9	30.41	0.911 2
MSRN	×2	38.08	0.960 5	33.74	0.917 0	32.23	0.901 3	32.22	0.932 6
IMDN	×2	38.00	0.960 5	33.63	0.917 7	32.19	0.899 6	32.17	0.928 3
OISR-SK2	×2	38.12	0.960 9	33.80	0.919 3	32.26	0.900 6	32.48	0.931 7
LatticeNet	×2	38.15	0.961 0	33.78	0.919 3	32.25	0.900 5	32.43	0.930 2
DID-D5	×2	38.15	0.961 0	33.77	0.919 0	32.27	0.900 6	32.38	0.930 5
MDFN	×2	38.14	0.961 0	33.83	0.919 6	32.27	0.900 6	32.41	0.931 0
Bicubic	×3	30.39	0.868 2	27.54	0.773 6	27.21	0.738 4	24.46	0.734 4
SRCNN	×3	32.75	0.909 0	29.30	0.821 5	28.41	0.786 3	26.24	0.798 9
VDSR	×3	33.66	0.921 3	29.77	0.831 4	28.82	0.797 6	27.14	0.827 9
DRCN	×3	33.85	0.921 5	29.89	0.831 7	28.81	0.795 4	27.16	0.831 1
LapSRN	×3	33.82	0.922 7	29.89	0.832 0	28.83	0.797 3	27.08	0.827 2
MSRN	×3	34.38	0.926 2	30.34	0.839 5	29.08	0.804 1	28.08	0.855 4

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

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Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

模型	缩放因子	Set5		Set14		BSD100		Urban100
模型	缩放因子	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
IMDN	×3	34.36	0.927 0	30.32	0.841 7	29.09	0.804 6	28.17	0.851 9
OISR-SK2	×3	34.55	0.928 2	30.46	0.844 3	29.18	0.807 5	28.50	0.859 7
LatticeNet	×3	34.53	0.928 1	30.39	0.842 4	29.15	0.805 9	28.33	0.853 8
DID-D5	×3	34.55	0.928 0	30.49	0.844 6	29.19	0.806 9	28.39	0.856 6
MDFN	×3	34.60	0.928 4	30.50	0.844 9	29.21	0.807 5	28.52	0.859 1
Bicubic	×4	28.42	0.810 4	26.00	0.701 9	25.96	0.667 4	23.14	0.657 0
SRCNN	×4	30.48	0.862 8	27.49	0.750 3	26.90	0.710 1	24.53	0.722 1
VDSR	×4	31.35	0.883 0	28.01	0.768 0	27.29	0.725 1	25.18	0.754 3
DRCN	×4	31.56	0.881 0	28.15	0.762 0	27.24	0.715 0	25.15	0.753 0
LapSRN	×4	31.54	0.885 5	28.19	0.772 0	27.32	0.728 0	25.21	0.755 3
MSRN	×4	32.07	0.890 3	28.60	0.775 1	27.52	0.727 3	26.04	0.789 6
IMDN	×4	32.21	0.894 8	28.58	0.781 1	27.56	0.735 3	26.04	0.783 8
OISR-SK2	×4	32.32	0.896 5	28.72	0.784 3	27.66	0.739 0	26.37	0.795 3
LatticeNet	×4	32.30	0.896 2	28.68	0.783 0	27.62	0.736 7	26.25	0.787 3
DID-D5	×4	32.33	0.896 8	28.75	0.785 2	27.68	0.738 6	26.36	0.793 3
MDFN	×4	32.41	0.897 6	28.78	0.786 0	27.69	0.739 3	26.39	0.794 4

Table 3. Complexity and performance of different models
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Table 3. Complexity and performance of different models
模型参数量/M Flops/G PSNR/dB SSIM
MSRN 6.08 107.27 32.07 0.890 3
OISR-SK2 5.51 117.41 32.32 0.896 5
DID-D5 5.21 93.03 32.33 0.896 8
MDFN 4.89 87.76 32.41 0.897 6

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Deqiang CHENG, Jiamin ZHAO, Qiqi KOU, Liangliang CHEN, Chenggong HAN. Multi-scale dense feature fusion network for image super-resolution[J]. Optics and Precision Engineering, 2022, 30(20): 2489

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Paper Information

Category: Information Sciences

Received: May. 9, 2022

Accepted: --

Published Online: Oct. 27, 2022

The Author Email: KOU Qiqi (kouqiqi@cumt.edu.cn)

DOI:10.37188/OPE.20223020.2489

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Effects of different connection modes and different feature extraction modules on model performance

Table 1. Effects of different connection modes and different feature extraction modules on model performance

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

Table 2. Index comparison under benchmark dataset when scaling factor is 2， 3 and 4

Table 3. Complexity and performance of different models

Table 3. Complexity and performance of different models

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