深度学习的图像超分辨率重建技术综述

doi:10.3778/j.issn.1673-9418.2202063

计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (9): 1990-2010.DOI: 10.3778/j.issn.1673-9418.2202063

深度学习的图像超分辨率重建技术综述

杨才东¹, 李承阳¹^,², 李忠博¹^,⁺(), 谢永强¹, 孙方伟¹, 齐锦¹

1.军事科学院系统工程研究院,北京 100141
2.北京大学信息科学与技术学院,北京 100871

收稿日期:2022-02-23 修回日期:2022-05-26 出版日期:2022-09-01 发布日期:2022-09-15
通讯作者: + E-mail: zbli2021@163.com
作者简介:杨才东（1996—）,男,贵州六盘水人,硕士研究生,主要研究方向为超分辨率重建、目标检测。
李承阳（1995—）,男,辽宁鞍山人,博士研究生,主要研究方向为目标检测、数据挖掘、机器学习。
李忠博（1983—）,男,北京人,博士,高级工程师,主要研究方向为多媒体技术、机器学习、云计算等。
谢永强（1972—）,男,北京人,博士,研究员,主要研究方向为机器学习、多媒体技术、智能系统架构等。
孙方伟（1996—）,男,山东青岛人,硕士研究生,主要研究方向为目标检测、目标跟踪、语义分割。
齐锦（1971—）,女,北京人,硕士,高级工程师,主要研究方向为多媒体技术、智能系统架构、云计算等。

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

YANG Caidong¹, LI Chengyang¹^,², LI Zhongbo¹^,⁺(), XIE Yongqiang¹, SUN Fangwei¹, QI Jin¹

1. Institute of Systems Engineering, Academy of Military Sciences, Beijing 100141, China
2. School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China

Received:2022-02-23 Revised:2022-05-26 Online:2022-09-01 Published:2022-09-15
About author:YANG Caidong, born in 1996, M.S. candidate. His research interests include super-resolution reconstruction and object detection.
LI Chengyang, born in 1995, Ph.D. candidate. His research interests include object detection, data mining and machine learning.
LI Zhongbo, born in 1983, Ph.D., senior engineer. His research interests include multimedia technology, machine learning, cloud computing, etc.
XIE Yongqiang, born in 1972, Ph.D., researcher. His research interests include machine learning, multimedia technology, intelligent system architecture, etc.
SUN Fangwei, born in 1996, M.S. candidate. His research interests include object detection, object tracking and semantic segmentation.
QI Jin, born in 1971, M.S., senior engineer. Her research interests include multimedia technology, intelligent system architecture, cloud computing, etc.

摘要/Abstract

摘要：

图像超分辨率重建技术的本质是突破现有硬件条件的限制,通过算法将低分辨率图像重建为高分辨率图像,获得包含更多信息的图像的技术。随着深度学习理论和技术的迅速发展,深度学习被引入到超分辨率重建领域并取得了进展。对基于深度学习的图像超分辨率重建算法进行了全面总结,并对已有算法进行了分类、分析和比较。首先,详细介绍了单图像超分辨率重建模型的组成结构,包括超分框架、上采样方法、非线性映射学习模块以及损失函数等。其次,从图像对齐和Patch匹配两方面出发,对现有的基于参考的图像超分辨率重建算法进行了分析。然后,介绍了图像超分辨重建领域的benchmark数据集以及图像质量评估参数,对目前主流算法的性能进行了评估。最后,对基于深度学习的图像超分辨率重建算法的未来研究趋势进行了展望。

关键词: 超分辨率重建, 深度学习, 单图像, 基于参考, 图像对齐

Abstract:

The essence of image super-resolution reconstruction technology is to break through the limitation of hardware conditions, and reconstruct a high-resolution image from a low-resolution image which contains less infor-mation through the image super-resolution reconstruction algorithms. With the development of the technology on deep learning, deep learning has been introduced into the image super-resolution reconstruction field. This paper summarizes the image super-resolution reconstruction algorithms based on deep learning, classifies, analyzes and compares the typical algorithms. Firstly, the model framework, upsampling method, nonlinear mapping learning module and loss function of single image super-resolution reconstruction method are introduced in detail. Secondly, the reference-based super-resolution reconstruction method is analyzed from two aspects: pixel alignment and Patch matching. Then, the benchmark datasets and image quality evaluation indices used for image super-resolution recon-struction algorithms are summarized, the characteristics and performance of the typical super-resolution recons-truction algorithms are compared and analyzed. Finally, the future research trend on the image super-resolution reconstruction algorithms based on deep learning is prospected.

Key words: super-resolution reconstruction, deep learning, single image, reference-based, image alignment

中图分类号:

TP391

杨才东, 李承阳, 李忠博, 谢永强, 孙方伟, 齐锦. 深度学习的图像超分辨率重建技术综述[J]. 计算机科学与探索, 2022, 16(9): 1990-2010.

YANG Caidong, LI Chengyang, LI Zhongbo, XIE Yongqiang, SUN Fangwei, QI Jin. Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(9): 1990-2010.

图/表 18

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数据集名称	数据集介绍	年份	期刊	图像数量
Set5^[25]	5张低复杂度测试图片,常用验证集	2014	ACCV	5张
Set14^[25]	14张测试图片	2014	ACCV	14张
BSD100^[63]	包含植物、人和食物等100张测试图像的经典数据集,是BSD300中的测试集,来源于Berkeley segmentation数据集	2002	IEEE	100张
Urban100^[64]	100张具有各种真实结构的HR图像,具有自相似性,常用验证集	2015	IEEE	100张
DIV2K^[65]	总共1 000张2K高清图片,按照8∶1∶1的比例分成训练、验证和测试图像,涵盖范围广	2017	IEEE	1 000张
Flickr2K^[35]	2 650张图片,包含人物、动物和风景等	2017	CVPR	2 650张
RealSR^[66]	同一场景不同焦距下的LR-HR图像对,并且具有不同的缩放尺度,完成了精准对齐	2019	IEEE	595对
City100^[67]	模拟相机镜头的真实数据集,具有100对LR-HR数据对	2020	IEEE	100对
DRealSR^[68]	具有LR-HR图像对的真实数据集,比RealSR具有更强的多样性、更多的数据	2020	ECCV	884对×2;783对×3;840对×4

数据集名称	数据集介绍	年份	期刊	图像数量
Set5^[25]	5张低复杂度测试图片,常用验证集	2014	ACCV	5张
Set14^[25]	14张测试图片	2014	ACCV	14张
BSD100^[63]	包含植物、人和食物等100张测试图像的经典数据集,是BSD300中的测试集,来源于Berkeley segmentation数据集	2002	IEEE	100张
Urban100^[64]	100张具有各种真实结构的HR图像,具有自相似性,常用验证集	2015	IEEE	100张
DIV2K^[65]	总共1 000张2K高清图片,按照8∶1∶1的比例分成训练、验证和测试图像,涵盖范围广	2017	IEEE	1 000张
Flickr2K^[35]	2 650张图片,包含人物、动物和风景等	2017	CVPR	2 650张
RealSR^[66]	同一场景不同焦距下的LR-HR图像对,并且具有不同的缩放尺度,完成了精准对齐	2019	IEEE	595对
City100^[67]	模拟相机镜头的真实数据集,具有100对LR-HR数据对	2020	IEEE	100对
DRealSR^[68]	具有LR-HR图像对的真实数据集,比RealSR具有更强的多样性、更多的数据	2020	ECCV	884对×2;783对×3;840对×4

模型算法	超分框架	上采样方式	网络模型	损失函数	优点	局限性
SRCNN^[12]	前采样	三立方插值	卷积直连	MSE损失	首次将深度学习引入超分领域,重建效果超过传统算法	训练收敛慢,只能完成单一尺度放大,重建图像平滑
FSRCNN^[31]	后采样	转置卷积	卷积直连	MSE损失	速度较SRCNN提高,实时性得到提高	依赖于局部的像素信息进行重建,有伪影产生
VDSR^[34]	后采样	三立方插值	残差网络	MSE损失	实现多尺度超分放大	对图像进行插值放大再计算,导致巨大的计算量
ESPCN^[19]	前采样	亚像素卷积	卷积直连	MSE损失	网络效率提高,提出了亚像素卷积放大方法,灵活解决了多尺度放大问题	重建图像有伪影
SRResNet^[14]	后采样	亚像素卷积	残差网络	MSE损失	解决深层网络难训练问题	重建图像光滑
SRGAN^[14]	后采样	亚像素卷积	残差网络	感知损失	提高图像感知质量	模型设计复杂,训练困难
LapSRN^[71]	渐进式	三立方插值	残差网络	L1损失	产生多尺度超分图像,网络拥有更大的感受野	重建质量不佳
EDSR^[35]	后采样	亚像素卷积	残差网络	L1损失	增大模型尺寸,降低训练难度	推理时间长,实时性差
SRDenseNet^[29]	后采样	转置卷积	残差、稠密网络	MSE损失	减轻梯度消失,增强特征传播能力	对所有层进行连接,计算量大
RDN^[72]	后采样	亚像素卷积	残差网络	L1损失	增加网络复杂度,提高主观视觉质量效果	采用了稠密连接,计算量大
RCAN^[37]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	通过注意力网络使模型专注于对高频信息的学习	引入通道注意机制的同时,将各个卷积层视为单独的过程,忽略了不同层之间的联系
ESRGAN^[73]	后采样	亚像素卷积	残差、稠密网络	L1损失	更稳定的GAN模型,重建高频纹理细节	模型设计复杂,训练困难
SAN^[74]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	提出了二阶通道注意力模块,增强了模型的特征表达和特征学习能力,利用非局部加强残差组捕捉长距离空间内容信息	计算成本高
SRFBN^[75]	后采样	转置卷积	递归、残差、稠密网络	L1损失	引入反馈机制,前面层可以从后面层中受益	通过迭代的方式虽然减少了参数,但是每次迭代都会计算loss和重建图像,计算量大
CDC^[68]	渐进式	转置卷积	递归、残差、注意力机制网络	梯度加权损失	提高真实世界图像重建质量,对图像不同区域进行针对性训练	训练复杂,计算量大
HAN^[76]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	学习不同深度之间特征的关系,提高特征表达能力	对不同层、通道和位置之间的特征信息进行建模,参数量多,计算量大
SRFlow^[77]	后采样	亚像素卷积	残差网络	对抗损失内容损失	克服了GAN模型易崩溃的问题	生成多张近似的图片,计算量大
DFCAN^[78]	后采样	亚像素卷积	残差、注意力机制网络	对抗损失	提升显微镜下超分重建图像质量	设计复杂,专用于显微镜超分
LIIT^[79]	后采样	亚像素卷积	残差网络	L1损失	连续表达学习,实现30倍的放大图像	生成图像光滑

模型算法	超分框架	上采样方式	网络模型	损失函数	优点	局限性
SRCNN^[12]	前采样	三立方插值	卷积直连	MSE损失	首次将深度学习引入超分领域,重建效果超过传统算法	训练收敛慢,只能完成单一尺度放大,重建图像平滑
FSRCNN^[31]	后采样	转置卷积	卷积直连	MSE损失	速度较SRCNN提高,实时性得到提高	依赖于局部的像素信息进行重建,有伪影产生
VDSR^[34]	后采样	三立方插值	残差网络	MSE损失	实现多尺度超分放大	对图像进行插值放大再计算,导致巨大的计算量
ESPCN^[19]	前采样	亚像素卷积	卷积直连	MSE损失	网络效率提高,提出了亚像素卷积放大方法,灵活解决了多尺度放大问题	重建图像有伪影
SRResNet^[14]	后采样	亚像素卷积	残差网络	MSE损失	解决深层网络难训练问题	重建图像光滑
SRGAN^[14]	后采样	亚像素卷积	残差网络	感知损失	提高图像感知质量	模型设计复杂,训练困难
LapSRN^[71]	渐进式	三立方插值	残差网络	L1损失	产生多尺度超分图像,网络拥有更大的感受野	重建质量不佳
EDSR^[35]	后采样	亚像素卷积	残差网络	L1损失	增大模型尺寸,降低训练难度	推理时间长,实时性差
SRDenseNet^[29]	后采样	转置卷积	残差、稠密网络	MSE损失	减轻梯度消失,增强特征传播能力	对所有层进行连接,计算量大
RDN^[72]	后采样	亚像素卷积	残差网络	L1损失	增加网络复杂度,提高主观视觉质量效果	采用了稠密连接,计算量大
RCAN^[37]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	通过注意力网络使模型专注于对高频信息的学习	引入通道注意机制的同时,将各个卷积层视为单独的过程,忽略了不同层之间的联系
ESRGAN^[73]	后采样	亚像素卷积	残差、稠密网络	L1损失	更稳定的GAN模型,重建高频纹理细节	模型设计复杂,训练困难
SAN^[74]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	提出了二阶通道注意力模块,增强了模型的特征表达和特征学习能力,利用非局部加强残差组捕捉长距离空间内容信息	计算成本高
SRFBN^[75]	后采样	转置卷积	递归、残差、稠密网络	L1损失	引入反馈机制,前面层可以从后面层中受益	通过迭代的方式虽然减少了参数,但是每次迭代都会计算loss和重建图像,计算量大
CDC^[68]	渐进式	转置卷积	递归、残差、注意力机制网络	梯度加权损失	提高真实世界图像重建质量,对图像不同区域进行针对性训练	训练复杂,计算量大
HAN^[76]	后采样	亚像素卷积	残差、注意力机制网络	L1损失	学习不同深度之间特征的关系,提高特征表达能力	对不同层、通道和位置之间的特征信息进行建模,参数量多,计算量大
SRFlow^[77]	后采样	亚像素卷积	残差网络	对抗损失内容损失	克服了GAN模型易崩溃的问题	生成多张近似的图片,计算量大
DFCAN^[78]	后采样	亚像素卷积	残差、注意力机制网络	对抗损失	提升显微镜下超分重建图像质量	设计复杂,专用于显微镜超分
LIIT^[79]	后采样	亚像素卷积	残差网络	L1损失	连续表达学习,实现30倍的放大图像	生成图像光滑

模型算法	对齐方法	匹配方法	融合方法	损失函数	优点	局限性
Landmark^[46]	全局配准	—	求解能量最小化	—	利用全局匹配,解决了图像内容相似但照明、焦距、镜头透视图等不同造成关联细节不确定性问题	参考图像与输入图像分辨率差距过大,影响了模型的学习能力
CrossNet^[48]	光流法	—	融合解码层	L1损失	解决了Ref图像与LR图像分辨率差距大带来的图像对齐困难的问题	仅限于小视差的条件,在光场数据集上可以达到很高的精度,但在处理大视差的情况下效果迅速下降
HCSR^[49]	光流法	—	混合策略融合	重构损失对抗损失	引入SISR方法生成的中间视图,解决跨尺度输入之间的显著分辨率之差引起的变换问题	依赖LR于HR之间的对准质量,计算多个视图差会带来巨大的计算量
SSEN^[50]	可变性卷积	—	RCAN基础网络	重构损失感知损失对抗损失	使用非局部块作为偏移量估计来积极地搜索相似度,可以以多尺度的方式执行像素对齐,并且提出的相似性搜索与提取模块可以插入到现有任何超分网络中	利用非局部块来辅助相似度搜索,全局计算意味着巨大的参数量
SS-Net^[53]	—	跨尺度对应网络	构建一个预测模块,从尺度3到尺度1进行融合	交叉熵损失	设计了一个跨尺度对应网络来表示图像之间的匹配,在多个尺度下进行特征融合	参考图像与输入图像的相似度直接影响生成图像的质量
SRNTT^[54]	—	在自然空间中进行多级匹配	结合多级残差网络和亚像素卷积层构成神经结构转移模块	重构损失感知损失对抗损失	根据参考图像的纹理相似度自适应地转换纹理,丰富了HR纹理细节;并且在特征空间进行多级匹配,促进了多尺度神经传输,使得模型即使在参考图像极不相关的情况下性能也只会降低到SISR的级别	当相似纹理较少或者图像区域重复时,不能很好地处理,计算成本高
TTSR^[16]	—	利用Transformer架构中的注意力结构来完成特征的匹配	利用软注意力模块完成特征融合	重构损失感知损失对抗损失	引入了Transformer架构,利用Transformer的注意力机制发现更深层的特征对应,从而可以传递准确的纹理特性	当相似纹理较少或者图像区域重复时,不能很好地处理,计算成本高
Cross-MPI^[55]	—	平面感知MPI机制	对不同深度平面通道进行汇总	重构损失感知损失内部监督损失	平面感知MPI机制充分利用了场景结构进行有效的基于注意的对应搜索,不需要进行跨尺度立体图像之间的直接匹配或穷举匹配	虽然解决了图像之间较大分辨率差异时的高保真超分辨率重建,但是忽略了图像之间在分布上存在的差异产生的影响
MASA^[56]	—	利用自然图像局部相关性,由粗到精进行匹配	利用双残差聚合模块（DRAM）	重构损失感知损失对抗损失	在保持高质量匹配的同时,利用图像的局部相关性,缩小特征空间搜索范围。同时提出了空间自适应模块,使得Ref图像中的有效信息可以更充分地利用	基于图像的内容和外观相似度来进行计算,忽略了HR与LR图像之间的底层转换关系
C²-Matching^[57]	—	利用图像的增强视图来学习经过底层变换之后的对应关系	动态融合模块完成特征融合	重构损失感知损失对抗损失	不仅考虑了图像分辨率差距上带来的影响,还考虑了图像在底层变换过程中导致图像外观发生变换带来的影响,使得模型对大尺度下以及旋转变换等情况都具有较强的鲁棒性	模型结构较为复杂,计算量大

深度学习的图像超分辨率重建技术综述

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

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Metrics

算法名称	放大倍数	Set5		Set14		BSD100		Urban100
算法名称	放大倍数	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
SRCNN^[12]	×2	33.66	0.954 2	32.45	0.906 7	31.36	0.887 9	29.50	0.894 6
	×3	32.75	0.909 0	29.30	0.821 5	28.41	0.786 3	26.24	0.798 9
	×4	30.09	0.862 7	27.18	0.786 1	26.68	0.729 1	24.52	0.722 1
FSRCNN^[31]	×2	37.05	0.956 0	32.66	0.909 0	31.53	0.892 0	29.88	0.902 0
	×3	33.18	0.914 0	29.37	0.824 0	28.53	0.791 0	26.43	0.808 0
	×4	30.71	0.865 7	27.59	0.753 5	26.98	0.739 8	24.62	0.728 0
ESPCN^[19]	×2	37.00	0.955 9	32.75	0.909 8	31.51	0.893 9	29.87	0.906 5
	×3	33.02	0.913 5	29.49	0.827 1	28.50	0.793 7	26.41	0.816 1
	×4	30.76	0.878 4	27.66	0.800 4	27.02	0.744 2	24.60	0.736 0
VDSR^[34]	×2	37.53	0.958 8	33.03	0.912 4	31.90	0.896 0	30.76	0.914 0
	×3	33.68	0.920 1	29.86	0.831 2	28.83	0.796 6	27.15	0.831 5
	×4	31.35	0.883 8	28.01	0.767 4	27.29	0.725 1	25.18	0.752 4
SRResNet^[14]	×4	32.05	0.901 9	28.49	0.818 4	27.58	0.762 0	26.07	0.783 9
SRGAN^[14]	×4	29.40	0.847 2	26.02	0.739 7	25.16	0.668 8	—	—
EDSR^[35]	×2	38.11	0.960 2	33.92	0.919 5	32.32	0.901 3	32.93	0.935 1
	×3	34.65	0.928 0	30.52	0.846 2	29.25	0.809 3	28.80	0.865 3
	×4	32.46	0.896 8	28.80	0.787 6	27.71	0.742 0	26.64	0.803 3
SRDenseNet^[29]	×4	32.02	0.893 4	28.50	0.778 2	27.53	0.733 7	26.05	0.781 9
RDN^[71]	×2	38.24	0.961 4	34.01	0.921 2	32.34	0.901 7	32.89	0.935 3
	×3	34.71	0.929 6	30.57	0.846 8	29.26	0.809 3	28.80	0.865 3
	×4	32.63	0.900 2	28.87	0.788 9	27.77	0.743 6	26.82	0.808 7
RCAN^[37]	×2	38.27	0.961 4	34.12	0.921 6	32.41	0.902 7	33.34	0.938 4
	×3	34.74	0.929 9	30.51	0.846 1	29.32	0.811 1	29.09	0.870 2
	×4	32.63	0.900 2	28.87	0.788 9	27.77	0.743 6	26.82	0.808 7
ESRGAN^[73]	×4	32.73	0.901 1	28.99	0.791 7	27.85	0.745 5	27.03	0.815 3
SAN^[74]	×2	38.31	0.962 0	34.07	0.921 3	32.42	0.902 8	33.10	0.937 0
	×3	34.75	0.930 0	30.59	0.847 6	29.33	0.811 2	28.93	0.867 1
	×4	32.64	0.900 3	28.92	0.788 8	27.78	0.743 6	26.79	0.806 8
SRFBN^[80]	×2	38.11	0.960 9	33.82	0.919 6	32.29	0.901 0	32.62	0.932 8
	×3	34.70	0.929 2	30.51	0.846 1	29.24	0.808 4	28.73	0.864 1
	×4	32.47	0.898 3	28.81	0.786 8	27.72	0.740 9	26.60	0.801 5
HAN^[76]	×2	38.33	0.961 7	34.24	0.922 4	32.45	0.903 0	33.53	0.939 8
	×3	34.75	0.929 9	30.67	0.848 3	29.32	0.811 0	29.10	0.870 5
	×4	32.64	0.900 2	28.90	0.789 0	27.80	0.744 2	26.85	0.809 4

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算法名称	CUFED5		Sun80		Urban100		Manga109		WR-SR
算法名称	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
CrossNet^[48]	25.48	0.764	28.52	0.793	25.11	0.764	23.36	0.741	—	—
SRNTT^[54]	26.24	0.784	28.54	0.793	25.50	0.783	28.95	0.885	27.59	0.780
TTSR^[16]	27.09	0.804	30.02	0.814	25.87	0.784	30.09	0.907	27.97	0.792
MASA^[55]	27.54	0.814	30.15	0.815	26.09	0.786	—	—	—	—
C²-Matching^[57]	28.24	0.841	30.18	0.817	26.03	0.785	30.47	0.911	28.32	0.801

算法名称	CUFED5		Sun80		Urban100		Manga109		WR-SR
算法名称	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM	PSNR/dB	SSIM
CrossNet^[48]	25.48	0.764	28.52	0.793	25.11	0.764	23.36	0.741	—	—
SRNTT^[54]	26.24	0.784	28.54	0.793	25.50	0.783	28.95	0.885	27.59	0.780
TTSR^[16]	27.09	0.804	30.02	0.814	25.87	0.784	30.09	0.907	27.97	0.792
MASA^[55]	27.54	0.814	30.15	0.815	26.09	0.786	—	—	—	—
C²-Matching^[57]	28.24	0.841	30.18	0.817	26.03	0.785	30.47	0.911	28.32	0.801