基于深度学习的单幅图像超分辨率重建算法综述

李佳星; 赵勇先; 王京华

doi:10.16383/j.aas.c190859

基于深度学习的单幅图像超分辨率重建算法综述

doi: 10.16383/j.aas.c190859

李佳星^1,,
赵勇先^{2, 3,},
王京华^{1, 4,}

1.
长春理工大学机电工程学院长春 130022
2.
中国科学院长春光学精密机械与物理研究所长春 130033
3.
中国科学院大学北京 100049
4.
长春理工大学跨尺度微纳制造教育部重点实验室长春 130022

基金项目: 国防基础科研计划(JCKY2019411B001), “111”计划(D17017), 露泉创新基金(LQ-2020-01)资助

详细信息

作者简介:
李佳星：长春理工大学机电工程学院硕士研究生. 主要研究方向为计算机视觉, 深度学习, 单幅图像超分辨率重建. E-mail: jiaxing4912@163.com

赵勇先：中国科学院大学机械电子工程专业硕士研究生. 2020年获得长春理工大学学士学位. 主要研究生方向为计算机视觉和深度学习. E-mail: zyx_19980824@163.com

王京华：长春理工大学机电工程学院讲师. 2010年获得哈尔滨工业大学博士学位. 主要研究方向为移动机器人, 无人系统, 智能控制. 本文通信作者. E-mail: hit1920s@163.com

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出版历程
- 收稿日期: 2019-12-17
- 录用日期: 2020-06-11
- 网络出版日期: 2021-02-04
- 刊出日期: 2021-10-20

A Review of Single Image Super-resolution Reconstruction Algorithms Based on Deep Learning

LI Jia-Xing^1
,,
ZHAO Yong-Xian^{2, 3
,},
WANG Jing-Hua^{1, 4
,}

1.
College of Mechanical and Electric Engineering, Changchun University of Science and Technology, Changchun 130022
2.
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033
3.
University of Chinese Academy of Sciences, Beijing 100049
4.
Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022

Funds: Supported by Defense Industrial Technology Development Program (JCKY2019411B001), the “111” Project of China (D17017) and Innovation Fundation of Luquan (LQ-2020-01)

More Information

Author Bio:
LI Jia-Xing　Master student at the College of Mechanical and Electric Engineering, Changchun University of Science and Technology. Her research interest covers computer vision, deep learning, and single image super-resolution reconstruction

ZHAO Yong-Xian　Master student at the University of Chinese Academy of Sciences, majoring in Mechanical and Electronic Engineering. He received his bachelor degree from Changchun University of Science and Technology in 2020. His research interest covers computer vision and deep learning

WANG Jing-Hua　Lecturer at the College of Mechanical and Electric Engineering, Changchun University of Science and Technology. He received his Ph. D. degrees from Harbin Institute of Technology in 2010. His research interest covers mobile robots, unmanned systems and intelligent control. Corresponding author of this paper

摘要

摘要: 单幅图像超分辨率(Single image super-resolution, SISR)重建是计算机视觉领域上的一个重要问题, 在安防视频监控、飞机航拍以及卫星遥感等方面具有重要的研究意义和应用价值. 近年来, 深度学习在图像分类、检测、识别等诸多领域中取得了突破性进展, 也推动着图像超分辨率重建技术的发展. 本文首先介绍单幅图像超分辨率重建的常用公共图像数据集; 然后, 重点阐述基于深度学习的单幅图像超分辨率重建方向的创新与进展; 最后, 讨论了单幅图像超分辨率重建方向上存在的困难和挑战, 并对未来的发展趋势进行了思考与展望.
- 单幅图像超分辨率 /
- 计算机视觉 /
- 深度学习 /
- 神经网络
Abstract: Single image super-resolution (SISR) reconstruction is an important problem in the field of computer vision. It has important research significance and application value in security video surveillance, aircraft aerial photography and satellite remote sensing. In recent years, deep learning has made a breakthrough in many fields such as image classification, detection and recognition, and promoted the development of image super-resolution reconstruction technology. This paper first introduces the common public image datasets for single image super-resolution reconstruction. Then, the innovation and progress of single image super-resolution reconstruction based on deep learning are emphasized. Finally, the difficulties and challenges in the single image super-resolution reconstruction are discussed, and the future development trend is discussed.
- Single image super-resolution (SISR) /
- computer vision /
- deep learning /
- neural network

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图 1 SRCNN网络结构^[6]

Fig. 1 The SRCNN network structure^[6]

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图 2 超分辨率数据集示例

Fig. 2 Examples of super-resolution datasets

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图 3 FSRCNN网络结构与SRCNN网络结构的对比^[13]

Fig. 3 Comparison of FSRCNN network structure and SRCNN network structure^[13]

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图 4 FSRCNN网络卷积层与反卷积层的具体结构^[13]

Fig. 4 The concrete structure of convolution layer and deconvolution layer of FSRCNN network^[13]

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图 5 VDSR网络结构^[7]

Fig. 5 The VDSR network structure^[7]

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图 6 ESPCN网络结构^[33]

Fig. 6 The ESPCN network structure^[33]

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图 7 RED-Net结构^[34]

Fig. 7 The RED-Net structure^[34]

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图 8 MemNet网络结构^[36]

Fig. 8 The MemNet network structure^[36]

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图 9 SRDenseNet网络结构^[38]

Fig. 9 The SRDenseNet network structure^[38]

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图 10 RDN网络结构^[40]

Fig. 10 The RDN network structure^[40]

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图 11 EDSR与WDSR网络结构的对比^[41]

Fig. 11 Comparison of EDSR network structure and SRCNN network structure^[41]

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图 12 三种残差模块的对比^[41]

Fig. 12 Comparison of three residual blocks^[41]

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图 13 NatSR网络结构^[45]

Fig. 13 The NatSR network structure^[45]

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图 14 NMD结构^[45]

Fig. 14 The NMD structure^[45]

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图 15 主流上采样方法

((a) 预上采样SR网络; (b) 后上采样SR网络; (c) 渐进上采样SR网络; (d) 迭代上下采样SR网络)

Fig. 15 Mainstream upsampling methods

((a) Pre-upscaling SR network; (b) Post-upscaling SR network; (c) Progressive upscaling SR network; (d) Iterative up-and-down sampling network)

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图 16 CinCGAN网络结构^[61]

Fig. 16 The CinCGAN network structure^[61]

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图 17 CinCGAN中生成器与鉴别器的结构^[61]

Fig. 17 The structure of generator and discriminator in CinCGAN^[61]

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图 18 ZSSR网络结构^[77]

Fig. 18 The ZSSR network structure^[77]

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图 19 振铃效应

Fig. 19 Ringing effect

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图 20 数码相机成像原理^[82]

Fig. 20 Principle of digital camera imaging^[82]

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图 21 双卷积神经网络^[82]

Fig. 21 A dual convolutional neural network^[82]

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图 22 图像恢复分支^[82]

Fig. 22 The image restoration branch^[82]

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图 23 部分模型的主观视觉与PSNR的比较

Fig. 23 Comparison of subjective vision and PSNR of partial models

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表 1 常用超分辨率训练数据集

Table 1 Widely used Super-resolution training datasets

数据集名称	图像数量	图像格式	图像描述	平均像素^[31]	平均分辨率
BSDS200^[15]	200	JPG	BSDS500的子集用于训练	154, 401	(432, 370)
T91^[17]	91	PNG	车、人脸、水果、花等	58, 853	(264, 204)
General-100^[13]	100	BMP	人物、动物、日常景象等	181, 108	(435, 381)

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表 2 常用超分辨率测试数据集

Table 2 Widely used Super-resolution testing datasets

数据集名称	图像数量	图像格式	图像描述	平均像素	平均分辨率
Set14^[11]	14	PNG	人物、动物、自然景象	230, 203	(492, 446)
BSDS100^[15]	100	JPG	BSDS500的子集用于测试	154, 401	(432, 370)
Set5^[10]	5	PNG	人物、动物、昆虫等	113, 491	(313, 336)
Urban100^[12]	100	PNG	建筑物	774, 314	(984, 797)
Manga109^[16]	109	PNG	漫画	966, 11	(826, 1169)

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表 3 MOS评估准则

Table 3 The MOS assessment

分数	绝对评估	相对评估
1	图像质量非常差	该组中最差
2	图像质量较差	差于该组中平均水平
3	图像质量一般	该组中的平均水平
4	图像质量较好	好于该组中的平均水平
5	图像质量非常好	该组中最好

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表 4 部分网络模型在基准数据集Set5、Set14的平均PSNR对比

Table 4 The average PSNR comparison of some network models on the Set5 and Set14 benchmark datasets

	Set5				Set14
方法	×2	×3	×4	×8	×2	×3	×4	×8
Bicubic^[2]	33.66	30.39	28.42	24.39	30.23	27.54	26.00	23.19
SRCNN^[6]	36.66	32.75	30.49	25.33	32.45	29.30	27.50	23.85
VDSR^[7]	37.10	32.89	30.84	25.72	32.97	29.77	28.03	24.21
ESPCN^[33]	—	33.13	30.90	—	—	29.49	27.73	—
SRGAN^[8]	—	—	30.64	—	—	—	26.92	—
LapSRN^[47]	37.52	33.82	31.54	26.14	33.08	29.87	28.19	24.44
SRDenseNet^[38]	—	—	32.02	—	—	—	28.50	—
EDSR^[43]	38.20	34.76	32.62	26.96	34.02	30.66	28.94	24.91
EnhanceNet^[44]	—	—	31.74	—	—	—	28.42	—
DBPN^[53]	38.09	—	32.47	27.21	33.85	—	28.82	25.13
RCAN^[55]	38.33	34.85	32.73	27.47	34.23	30.76	28.98	25.40
SRMD^[61]	37.79	34.12	31.96	—	33.32	30.04	28.35	—
ZSSR^[77]	37.37	33.42	31.13	—	33.00	29.80	28.01	—
Meta-SR^[72]	—	—	—	—	34.04	30.55	28.84	—
OISR^[85]	38.12	34.56	32.33	—	33.80	30.46	28.73	—

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表 5 部分网络模型在基准数据集Set5、Set14的平均SSIM对比

Table 5 The comparison of average SSIM of partial network models on the Set5 and Set14 benchmark datasets

	Set5				Set14
方法	×2	×3	×4	×8	×2	×3	×4	×8
Bicubic^[2]	0.9299	0.8682	0.8104	0.657	0.8687	0.7736	0.7019	0.568
SRCNN^[6]	0.9542	0.9090	0.8628	0.689	0.9067	0.8215	0.7513	0.593
VDSR^[7]	0.9587	0.9213	0.8838	0.711	0.9124	0.8314	0.7674	0.609
FSRCNN^[13]	0.9558	0.9140	0.8657	0.682	0.9088	0.8242	0.7535	0.592
SRGAN^[8]	—	—	0.8472	—	—	—	0.7397	—
LapSRN^[47]	0.959	0.9227	0.885	0.738	0.913	0.8320	0.772	0.623
SRDenseNet^[38]	—	—	0.8934	—	—	—	0.7782	—
EDSR^[43]	0.9606	0.9290	0.8984	0.775	0.9204	0.8481	0.7901	0.640
MemNet^[36]	0.9597	0.9248	0.8893	0.7414	0.9142	0.8350	0.7723	0.6199
DBPN^[53]	0.960	—	0.898	0.784	0.919	—	0.786	0.648
RCAN^55]	0.9617	0.9305	0.9013	0.7913	0.9225	0.8494	0.7910	0.6553
SRMD^[61]	0.960	0.925	0.893	—	0.915	0.837	0.777	—
ZSSR^[77]	0.9570	0.9188	0.8796	—	0.9108	0.8304	0.7651	—
Meta-SR^[72]	—	—	—	—	0.9213	0.8466	0.7872	—
OISR^[85]	0.9609	0.9284	0.8968	—	0.9196	0.8450	0.7845	—

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表 6 部分网络模型在基准数据集Set5、Set14和BSDS100的×4尺度上的MOS对比

Table 6 The MOS comparison of some network models at ×4 of the benchmark datasets Set5, Set14 and BSDS100

方法	Set5	Set14	BSDS100
Bicubic^[2]	1.97	1.80	1.47
SRCNN^[6]	2.57	2.26	1.87
ESPCN^[33]	2.89	2.52	2.01
DRCN^[37]	3.26	2.84	2.12
SRResNet^[8]	3.37	2.98	2.29
SRGAN^[8]	3.58	3.72	3.56
HR	4.32	4.32	4.46

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表 7 部分网络模型在各测试数据集上的运行时间对比

Table 7 The comparison of running time of partial network models on each testing datasets

方法	深度学习框架	CPU/GPU	测试数据集	上采样因子	运行时间 (s)
SRCNN^[6]	Caffe	CPU	Set5	×3	2.23
VDSR^[7]	MatConvNet	CPU	Set5	×3	0.13
ESPCN^[33]	Theano	CPU	Set14	×3	0.26
FSRCNN^[13]	Caffe	CPU	Set14	×3	0.061
LapSRN^[47]	MatConvNet	GPU	Set14	×4	0.04
MemNet^[36]	Caffe	GPU	Set5	×3	0.4
EnhanceNet^[44]	Tensorflow	GPU	Set5	×4	0.009
MS-LapSRN^[69]	MatConvNet	GPU	Urban100	×4	0.06
ZSSR^[77]	—	GPU	BSDS100	×2	9
Meta-SR^[72]	—	GPU	BSDS100	×2	0.033

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