Image Super-Resolution Reconstruction Based on Multi-Scale Residual Aggregation Feature Network

Lifeng He; Liangliang Su; Guangbin Zhou; Pu Yuan; Bofan Lu; Jiajia Yu

doi:10.3788/LOP202158.2410011

Laser & Optoelectronics Progress, Volume. 58, Issue 24, 2410011(2021)

Image Super-Resolution Reconstruction Based on Multi-Scale Residual Aggregation Feature Network

Lifeng He^1,2, Liangliang Su^1、*, Guangbin Zhou¹, Pu Yuan¹, Bofan Lu¹, and Jiajia Yu¹

¹School of Electronic Information and Artificial Intelligence, Shaanxi University of Science & Technology, Xi'an, Shaanxi 710021, China;

²School of Information Science and Technology, Aichi Prefectural University, Nagakute, Aichi 480- 1198, Japan

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Figures & Tables(12)

Fig. 1. Structure of multi-scale residual aggregation feature network

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Fig. 2. Multi-scale feature extraction module

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Fig. 3. Extended convolution kernel with different expansion coefficients

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Fig. 4. Comparison of different module structures. (a) Ordinary residual block; (b) hybrid extended convolution residual block

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Fig. 5. Gridding artifact with a single pixel convolved with a 3×3 extended convolutional kernel (expansion coefficient r=2)

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Fig. 6. Diagram of visual feature output. (a) RGB image; (b) without per-pixel addition operation; (c) with per-pixel addition operation

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Fig. 7. Reconstruction results of the three models in the Urban100 image “img091”. (a) Original drawing; (b) M_HERB; (c) M_RB+AM; (d) M_HERB+AM

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Fig. 8. Comparison of image reconstruction effects under various methods

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Table 1. Relationship between number of hybrid extended convolution residual blocks, average time, and PSNR
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Table 1. Relationship between number of hybrid extended convolution residual blocks, average time, and PSNR
N₂ 2 3 4 5 6 7 8 9 10 11
PSNR /dB 28.335 28.398 28.428 28.444 28.463 28.490 28.522 28.532 28.507 28.506
Average time /s 0.107 0.138 0.176 0.195 0.216 0.235 0.254 0.277 0.299 0.320

Table 2. Relationship between number of multi-scale feature extraction modules, average time , and PSNR
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Table 2. Relationship between number of multi-scale feature extraction modules, average time , and PSNR
N₁ 1 2 3 4
PSNR /dB 28.431 28.522 28.527 28.533
Average time /s 0.171 0.254 0.341 0.438

Table 3. Average PSNR/SSIM of three models on 5 data sets PSNR unit: dB

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Table 3. Average PSNR/SSIM of three models on 5 data sets PSNR unit: dB

Data set	M_HERB	M_RB+AM	M_HERB+AM
Set5	32.11/0.8938	32.01/0.8930	32.03/0.8933
Set14	28.49/0.7797	28.41/0.7756	28.52/0.7805
BSD100	27.53/0.7351	27.45/0.7312	27.57/0.7361
Manga109	30.17/0.9055	30.10/0.9011	30.30/0.9072
UrBan100	25.85/0.7792	25.77/0.7789	25.99/0.7846

Table 4. Average PSNR/SSIM of different methods on different test sets PSNR unit: dB

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Table 4. Average PSNR/SSIM of different methods on different test sets PSNR unit: dB

Method	r'	Set5	Set14	BSD100	Manga109	UrBan100
Bicubic	2	33.66/0.9299	30.24/0.8688	29.56/0.8431	30.80/0.9339	26.88/0.8403
SRCNN^[4]	2	36.66/0.9542	32.45/0.9067	31.36/0.8879	35.60/0.9663	29.50/0.8946
VDSR^[8]	2	37.53/0.9590	33.05/0.9130	31.90/0.8960	37.22/0.9750	30.77/0.9140
DRRN^[10]	2	37.74/0.9591	33.23/0.9136	32.05/0.8973	37.60/0.9736	31.23/0.9188
SRMDNF^[26]	2	37.79/0.9601	33.32/0.9159	32.05/0.8985	38.07/0.9761	31.33/0.9204
IMRSR^[12]	2	37.78/0.9643	33.26/0.8488	32.00/0.9073		31.00/0.9235
Proposed method	2	37.89/0.9603	33.41/0.9159	32.07/0.8986	38.24/0.9763	31.62/0.9237
Bicubic	3	30.39/0.8682	27.55/0.7742	27.21/0.7385	26.95/0.8556	24.46/0.7349
SRCNN^[4]	3	32.75/0.9090	29.30/0.8215	28.41/0.7863	30.48/0.9117	26.24/0.7989
VDSR^[8]	3	33.67/0.9210	29.78/0.8320	28.83/0.7990	32.01/0.9340	27.14/0.8290
DRRN^[10]	3	34.03/0.9244	29.96/0.8349	28.95/0.8004	32.42/0.9359	27.53/0.8378
SRMDNF^[26]	3	34.12/0.9254	30.04/0.8382	28.97/0.8025	33.00/0.9403	27.57/0.8398
IMRSR^[12]	3	33.91/0.9312	29.88/0.8488	28.80/0.8166		27.00/0.8403
Proposed method	3	34.18/0.9255	30.16/0.8389	28.99/0.8033	33.01/0.9413	27.77/0.8450
Method	r'	Set5	Set14	BSD100	Manga109	UrBan100
Bicubic	4	28.42/0.8104	26.00/0.7027	25.96/0.6675	24.89/0.7866	23.14/0.6577
SRCNN^[4]	4	30.48/0.8628	27.50/0.7513	26.90/0.7101	27.58/0.8555	24.52/0.7221
VDSR^[8]	4	31.35/0.8830	28.02/0.7680	27.29/0.7726	28.83/0.8870	25.18/0.7540
DRRN^[10]	4	31.68/0.8888	28.21/0.7721	27.38/0.7284	29.18/0.8914	25.44/0.7638
SRMDNF^[26]	4	31.96/0.8925	28.35/0.7787	27.49/0.7337	30.09/0.9024	25.68/0.7731
IMRSR^[12]	4	31.59/0.8957	28.19/0.7892	27.30/0.7469		25.15/0.7714
Proposed method	4	32.03/0.8933	28.52/0.7805	27.57/0.7361	30.30/0.9072	25.99/0.7846

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Lifeng He, Liangliang Su, Guangbin Zhou, Pu Yuan, Bofan Lu, Jiajia Yu. Image Super-Resolution Reconstruction Based on Multi-Scale Residual Aggregation Feature Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2410011

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Paper Information

Category: Image Processing

Received: Apr. 7, 2021

Accepted: May. 18, 2021

Published Online: Nov. 29, 2021

The Author Email: Liangliang Su (1211516382@qq.com)

DOI:10.3788/LOP202158.2410011

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Relationship between number of hybrid extended convolution residual blocks, average time, and PSNR

Table 1. Relationship between number of hybrid extended convolution residual blocks, average time, and PSNR

Table 2. Relationship between number of multi-scale feature extraction modules, average time , and PSNR

Table 2. Relationship between number of multi-scale feature extraction modules, average time , and PSNR

Table 3. Average PSNR/SSIM of three models on 5 data sets PSNR unit: dB

Table 3. Average PSNR/SSIM of three models on 5 data sets PSNR unit: dB

Table 4. Average PSNR/SSIM of different methods on different test sets PSNR unit: dB

Table 4. Average PSNR/SSIM of different methods on different test sets PSNR unit: dB