计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (5): 972-990.DOI: 10.3778/j.issn.1673-9418.2111126
收稿日期:
2021-11-10
修回日期:
2022-01-05
出版日期:
2022-05-01
发布日期:
2022-05-19
通讯作者:
+ E-mail: tojianglin@126.com作者简介:
钟梦圆(1998—),女,四川泸州人,硕士研究生,主要研究方向为图像处理、计算机视觉。基金资助:
Received:
2021-11-10
Revised:
2022-01-05
Online:
2022-05-01
Published:
2022-05-19
About author:
ZHONG Mengyuan, born in 1998, M.S. candidate. Her research interests include image processing and computer vision.Supported by:
摘要:
在人类视觉感知系统中,高分辨率(HR)图像是图像清晰表达其空间结构、细节特征、边缘纹理等信息的重要媒介,在医学、刑侦、卫星等领域有着极为广泛的实用价值。超分辨率图像重建(SRIR)旨在从给定的低分辨率(LR)图像中,重建含有清晰细节特征的高分辨率图像,是计算机视觉和图像处理领域中的一项重点研究任务。首先,对超分辨率图像重建的概念和数学模型进行阐述,并对图像重建方法进行系统分类,将其系统地分为基于插值、基于重构、基于学习(深度学习前、后)三类超分辨率图像重建方法;其次,对三类方法中典型的、常用的、最新的算法及其研究进行全面回顾和综述,并从网络结构、学习机制、适用场景、优势和局限性等方面对所列的图像重建算法进行了梳理;然后,归纳总结了超分辨率图像重建算法所用的数据集和图像质量评价指标,重点比较基于深度学习的各种超分辨率图像重建算法的特点与性能;最后,从四方面对超分辨率图像重建问题未来的研究方向或角度进行展望。
中图分类号:
钟梦圆, 姜麟. 超分辨率图像重建算法综述[J]. 计算机科学与探索, 2022, 16(5): 972-990.
ZHONG Mengyuan, JIANG Lin. Review of Super-Resolution Image Reconstruction Algorithms[J]. Journal of Frontiers of Computer Science and Technology, 2022, 16(5): 972-990.
算法 | 原理 | 运算复杂度 | 运算速度 | 算法灵活性 | 图像质量 |
---|---|---|---|---|---|
最近邻域插值法 | 线性插值 | 低 | 快 | 强 | 差 |
双线性插值法 | 线性插值 | 较低 | 较快 | 较强 | 较差 |
双三次线性插值法 | 线性插值 | 中 | 慢 | 弱 | 一般 |
边缘导向插值法 | 非线性插值 | 中 | 慢 | 较强 | 高 |
梯度引导插值法 | 非线性插值 | 高 | 慢 | 较弱 | 中 |
小波变换插值法 | 非线性插值 | 高 | 较慢 | 中 | 高 |
表1 基于插值的图像重建算法比较
Table 1 Comparison of image reconstruction algorithms based on interpolation
算法 | 原理 | 运算复杂度 | 运算速度 | 算法灵活性 | 图像质量 |
---|---|---|---|---|---|
最近邻域插值法 | 线性插值 | 低 | 快 | 强 | 差 |
双线性插值法 | 线性插值 | 较低 | 较快 | 较强 | 较差 |
双三次线性插值法 | 线性插值 | 中 | 慢 | 弱 | 一般 |
边缘导向插值法 | 非线性插值 | 中 | 慢 | 较强 | 高 |
梯度引导插值法 | 非线性插值 | 高 | 慢 | 较弱 | 中 |
小波变换插值法 | 非线性插值 | 高 | 较慢 | 中 | 高 |
算法 | 先验信息 | 可行解 | 运算复杂度 | 运算速度 | 算法灵活性 | 图像质量 |
---|---|---|---|---|---|---|
频域法 | 依赖性弱 | 唯一 | 低 | 慢 | 较差 | 差 |
非均匀内插法 | 依赖性强 | 唯一 | 较低 | 较慢 | 差 | 中 |
迭代反投影法 | 依赖性较强 | 不唯一 | 中 | 中 | 差 | 中 |
凸集投影法 | 依赖性较弱 | 不唯一 | 高 | 较慢 | 较强 | 较高 |
最大后验概率法 | 依赖性较弱 | 唯一 | 较高 | 较快 | 较强 | 较高 |
MAP/POCS法 | 依赖性弱 | 唯一 | 中 | 快 | 强 | 高 |
表2 基于重构的图像重建算法比较
Table 2 Comparison of image reconstruction algorithms based on reconstruction
算法 | 先验信息 | 可行解 | 运算复杂度 | 运算速度 | 算法灵活性 | 图像质量 |
---|---|---|---|---|---|---|
频域法 | 依赖性弱 | 唯一 | 低 | 慢 | 较差 | 差 |
非均匀内插法 | 依赖性强 | 唯一 | 较低 | 较慢 | 差 | 中 |
迭代反投影法 | 依赖性较强 | 不唯一 | 中 | 中 | 差 | 中 |
凸集投影法 | 依赖性较弱 | 不唯一 | 高 | 较慢 | 较强 | 较高 |
最大后验概率法 | 依赖性较弱 | 唯一 | 较高 | 较快 | 较强 | 较高 |
MAP/POCS法 | 依赖性弱 | 唯一 | 中 | 快 | 强 | 高 |
类型 | 作用 | 代表算法 |
---|---|---|
残差学习 | 提高网络收敛速度,学习丰富的复杂特征等 | VDSR[ |
递归学习 | 缓解梯度爆炸或消失,多路径递归学习等 | DRCN[ |
密集连接 | 加强不同层之间的图像传播、利用、融合等 | SRFBN[ |
跳跃连接 | 增强层间联系以及特征信息流的传递等 | DRCN[ |
注意力机制 | 标定图像重点与非重点学习重建区域,充分挖掘层内特征信息等 | RCAN[ |
连续记忆机制 | 全局性图像特征融合,连续记忆性传递低频、高频特征信息等 | RDN[ |
反馈机制 | 共享权重值,确保高级信息与低级信息间的表达与交流等 | SRFBN[ |
表3 深度学习背景下的部分网络结构
Table 3 Partial structure of network under deep learning background
类型 | 作用 | 代表算法 |
---|---|---|
残差学习 | 提高网络收敛速度,学习丰富的复杂特征等 | VDSR[ |
递归学习 | 缓解梯度爆炸或消失,多路径递归学习等 | DRCN[ |
密集连接 | 加强不同层之间的图像传播、利用、融合等 | SRFBN[ |
跳跃连接 | 增强层间联系以及特征信息流的传递等 | DRCN[ |
注意力机制 | 标定图像重点与非重点学习重建区域,充分挖掘层内特征信息等 | RCAN[ |
连续记忆机制 | 全局性图像特征融合,连续记忆性传递低频、高频特征信息等 | RDN[ |
反馈机制 | 共享权重值,确保高级信息与低级信息间的表达与交流等 | SRFBN[ |
算法 | 上采样方式 | 上采样方法 | 残差学习 | 递归学习 | 密集连接 | 注意力机制 | VGG网络 | 损失 |
---|---|---|---|---|---|---|---|---|
SRCNN[ | 预上采样 | 双三次插值 | — | — | — | — | — | |
FSRCNN[ | 后上采样 | 反卷积 | — | — | — | — | — | |
FSRCNN*[ | 后上采样 | 反卷积 | — | — | — | — | — | |
VDSR[ | 后上采样 | 双三次插值 | √ | — | — | — | — | MSE损失 |
ESPCN[ | 预上采样 | 亚像素卷积 | — | — | — | — | — | |
RCAN[ | 后上采样 | 亚像素卷积 | √ | — | — | √ | — | |
DRCN[ | 预上采样 | 双三次插值 | √ | √ | — | — | — | |
RDN[ | 后上采样 | 亚像素卷积 | — | √ | √ | — | — | |
DRRN[ | 预上采样 | 双三次插值 | √ | √ | — | — | — | |
SRFBN[ | 后上采样 | 反卷积 | √ | √ | √ | — | — | |
SRDenseNet[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
SRGAN[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | 对抗+内容损失 |
EDSR[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
ESRGAN[ | 后上采样 | 亚像素卷积 | — | √ | √ | — | — | |
RFB-ESRGAN[ | 后上采样 | 亚像素卷积 | √ | √ | √ | — | — | |
NatSR[ | 后上采样 | 反卷积 | √ | — | √ | — | √ | 重建+对抗+自然度损失 |
SMSR[ | 后上采样 | 双三次插值 | √ | — | √ | √ | — | 稀疏正则化损失 |
SRFeat[ | 逐步上采样 | 亚像素卷积 | √ | — | √ | — | — | 感知+图像损失 |
LatticeNet[ | 后上采样 | 反卷积 | √ | — | — | √ | — | MAE损失 |
IRN[ | 逐步上采样 | 可逆双射变换 | — | — | √ | — | — | LR制导+重构+匹配+感知损失 |
CDC[ | 逐步上采样 | 反卷积 | √ | √ | — | √ | — | 梯度加权损失 |
HAN[ | 后上采样 | 亚像素卷积 | √ | — | — | √ | — | |
MASA-SR[ | 迭代上采样 | 反卷积 | √ | — | — | — | — | 重建+感知+对抗损失 |
CF-Net[ | 迭代上采样 | 反卷积 | √ | — | — | — | — | MSSIM损失 |
Class-SR[ | 后上采样 | 反卷积 | — | — | — | — | — | |
LIIF[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
SRwarp[ | 自适应重采样 | 双三次插值 | — | — | — | — | — | |
Meta-SR[ | 后上采样 | Meta Upscale | — | √ | √ | — | — | |
表4 深度学习后的各算法特点对比
Table 4 Comparison of features of each algorithm after deep learning
算法 | 上采样方式 | 上采样方法 | 残差学习 | 递归学习 | 密集连接 | 注意力机制 | VGG网络 | 损失 |
---|---|---|---|---|---|---|---|---|
SRCNN[ | 预上采样 | 双三次插值 | — | — | — | — | — | |
FSRCNN[ | 后上采样 | 反卷积 | — | — | — | — | — | |
FSRCNN*[ | 后上采样 | 反卷积 | — | — | — | — | — | |
VDSR[ | 后上采样 | 双三次插值 | √ | — | — | — | — | MSE损失 |
ESPCN[ | 预上采样 | 亚像素卷积 | — | — | — | — | — | |
RCAN[ | 后上采样 | 亚像素卷积 | √ | — | — | √ | — | |
DRCN[ | 预上采样 | 双三次插值 | √ | √ | — | — | — | |
RDN[ | 后上采样 | 亚像素卷积 | — | √ | √ | — | — | |
DRRN[ | 预上采样 | 双三次插值 | √ | √ | — | — | — | |
SRFBN[ | 后上采样 | 反卷积 | √ | √ | √ | — | — | |
SRDenseNet[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
SRGAN[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | 对抗+内容损失 |
EDSR[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
ESRGAN[ | 后上采样 | 亚像素卷积 | — | √ | √ | — | — | |
RFB-ESRGAN[ | 后上采样 | 亚像素卷积 | √ | √ | √ | — | — | |
NatSR[ | 后上采样 | 反卷积 | √ | — | √ | — | √ | 重建+对抗+自然度损失 |
SMSR[ | 后上采样 | 双三次插值 | √ | — | √ | √ | — | 稀疏正则化损失 |
SRFeat[ | 逐步上采样 | 亚像素卷积 | √ | — | √ | — | — | 感知+图像损失 |
LatticeNet[ | 后上采样 | 反卷积 | √ | — | — | √ | — | MAE损失 |
IRN[ | 逐步上采样 | 可逆双射变换 | — | — | √ | — | — | LR制导+重构+匹配+感知损失 |
CDC[ | 逐步上采样 | 反卷积 | √ | √ | — | √ | — | 梯度加权损失 |
HAN[ | 后上采样 | 亚像素卷积 | √ | — | — | √ | — | |
MASA-SR[ | 迭代上采样 | 反卷积 | √ | — | — | — | — | 重建+感知+对抗损失 |
CF-Net[ | 迭代上采样 | 反卷积 | √ | — | — | — | — | MSSIM损失 |
Class-SR[ | 后上采样 | 反卷积 | — | — | — | — | — | |
LIIF[ | 后上采样 | 亚像素卷积 | √ | — | — | — | — | |
SRwarp[ | 自适应重采样 | 双三次插值 | — | — | — | — | — | |
Meta-SR[ | 后上采样 | Meta Upscale | — | √ | √ | — | — | |
数据集 | 格式 | 张数 | 大小 | 内容 | 用途 | 来源 |
---|---|---|---|---|---|---|
Set5 | PNG | 5 | 1.6 MB | baby、bird、butterfly、head、woman | 测试 | 2012BMVC |
Set14 | PNG | 14 | 4.2 MB | 人、动植物、漫画、幻灯片等 | 测试 | 2014CVPR |
BSD100 | PNG | 100 | 142.0 MB | 人、动植物、建筑、自然景观、环境等 | 测试 | — |
BSD300 | JPG | 300 | 93.0 MB | 人、动植物、食物、建筑、自然景观等 | 训练/验证 | 2001ICCV |
BSD500 | JPG | 500 | 155.0 MB | 人、动植物、食物、建筑、自然景观等 | 训练/验证 | 2011IEEE |
DIV2K2017 | PNG | 900 | 3.7 GB | 人、手工制品、环境、风景等 | 训练/验证 | 2017CVPR |
Urban100 | PNG | 100 | 143.0 MB | 建筑 | 测试 | 2015CVPR |
Manga109 | PNG | 109 | — | 漫画图 | 测试 | 2019ICML |
SunHay80 | PNG | 80 | 77.0 MB | 建筑、自然景观 | 测试 | — |
91-Image | PNG | 91 | 13.8 MB | 人、植物、汽车等 | 训练 | 2010IEEE |
Flickr2K | PNG | 2 650 | 10.8 GB | 人、动植物、建筑、自然景观等 | 训练 | 2017CVPR |
Real SR | JPG | 1 352 | 11.7 GB | 相机照片 | 训练/验证 | 2019CVPR |
Waterloo | PNG | 99 624 | 1.3 GB | 人、动植物、景观、交通等 | 训练 | 2017IEEE |
Outdoor-Scenes | PNG | 337 189 | 4.4 GB | 动植物、建筑、山水、天空等 | 训练/测试 | 2018CVPR |
PIRM | PNG | 200 | 59.8 MB | 人、物、环境、植物、自然风景等 | 验证/测试 | 2018ECCV |
W2S | PNG | 144 000 | 1.9 GB | 显微镜图像 | 训练/测试 | 2020ECCV |
PIPAL | PNG | 250 | 82.0 MB | 人、动植物、建筑等 | 测试 | 2020ECCV |
L20 | PNG | 20 | 55.9 MB | 人、动植物、建筑、景观等 | 测试 | 2016CVPR |
General-100 | BMP | 100 | 31.0 MB | 人、动植物、日用品、食物等 | 测试 | 2016ECCV |
表5 超分辨率图像重建的开源数据集
Table 5 Open source dataset for super-resolution image reconstruction
数据集 | 格式 | 张数 | 大小 | 内容 | 用途 | 来源 |
---|---|---|---|---|---|---|
Set5 | PNG | 5 | 1.6 MB | baby、bird、butterfly、head、woman | 测试 | 2012BMVC |
Set14 | PNG | 14 | 4.2 MB | 人、动植物、漫画、幻灯片等 | 测试 | 2014CVPR |
BSD100 | PNG | 100 | 142.0 MB | 人、动植物、建筑、自然景观、环境等 | 测试 | — |
BSD300 | JPG | 300 | 93.0 MB | 人、动植物、食物、建筑、自然景观等 | 训练/验证 | 2001ICCV |
BSD500 | JPG | 500 | 155.0 MB | 人、动植物、食物、建筑、自然景观等 | 训练/验证 | 2011IEEE |
DIV2K2017 | PNG | 900 | 3.7 GB | 人、手工制品、环境、风景等 | 训练/验证 | 2017CVPR |
Urban100 | PNG | 100 | 143.0 MB | 建筑 | 测试 | 2015CVPR |
Manga109 | PNG | 109 | — | 漫画图 | 测试 | 2019ICML |
SunHay80 | PNG | 80 | 77.0 MB | 建筑、自然景观 | 测试 | — |
91-Image | PNG | 91 | 13.8 MB | 人、植物、汽车等 | 训练 | 2010IEEE |
Flickr2K | PNG | 2 650 | 10.8 GB | 人、动植物、建筑、自然景观等 | 训练 | 2017CVPR |
Real SR | JPG | 1 352 | 11.7 GB | 相机照片 | 训练/验证 | 2019CVPR |
Waterloo | PNG | 99 624 | 1.3 GB | 人、动植物、景观、交通等 | 训练 | 2017IEEE |
Outdoor-Scenes | PNG | 337 189 | 4.4 GB | 动植物、建筑、山水、天空等 | 训练/测试 | 2018CVPR |
PIRM | PNG | 200 | 59.8 MB | 人、物、环境、植物、自然风景等 | 验证/测试 | 2018ECCV |
W2S | PNG | 144 000 | 1.9 GB | 显微镜图像 | 训练/测试 | 2020ECCV |
PIPAL | PNG | 250 | 82.0 MB | 人、动植物、建筑等 | 测试 | 2020ECCV |
L20 | PNG | 20 | 55.9 MB | 人、动植物、建筑、景观等 | 测试 | 2016CVPR |
General-100 | BMP | 100 | 31.0 MB | 人、动植物、日用品、食物等 | 测试 | 2016ECCV |
类别 | 特点 | 过程 | 适用场景 | 优势 | 局限性 |
---|---|---|---|---|---|
全参考图像 | HR图像 (真值图像) | | 相机图像、光学图像、医学图像等 | 高适用性和灵活性 | 训练时间长、经济成本高 |
半参考图像 | LR+HR (真值+失真图像) | | 遥感图像、医学图像、交通监控图像、SAR图像等 | 高对比性与参考性 | 图像差异较大,影响图像质量评价 |
盲参考图像 | LR图像 (失真图像) | | 遥感图像、医学图像、交通监控图像、SAR图像等 | 图像来源不受限制 | 图像质量评价受限 |
表6 影响不同图像重建效果的客观因素
Table 6 Objective factors affecting different image reconstruction effects
类别 | 特点 | 过程 | 适用场景 | 优势 | 局限性 |
---|---|---|---|---|---|
全参考图像 | HR图像 (真值图像) | | 相机图像、光学图像、医学图像等 | 高适用性和灵活性 | 训练时间长、经济成本高 |
半参考图像 | LR+HR (真值+失真图像) | | 遥感图像、医学图像、交通监控图像、SAR图像等 | 高对比性与参考性 | 图像差异较大,影响图像质量评价 |
盲参考图像 | LR图像 (失真图像) | | 遥感图像、医学图像、交通监控图像、SAR图像等 | 图像来源不受限制 | 图像质量评价受限 |
算法 | 大小 | Set5 | Set14 | Urban100 | BSD100 | Manga109 | DIV2K | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
SRCNN[ | | 36.7 | 0.95 | 32.5 | 0.91 | 29.5 | 0.89 | 31.4 | 0.89 | 35.6 | 0.97 | — | — |
FSRCNN[ | 37.1 | 0.96 | 32.7 | 0.91 | 29.9 | 0.90 | 31.5 | 0.89 | 36.7 | 0.97 | — | — | |
FSRCNN*[ | 37.3 | 0.96 | 32.8 | 0.91 | 30.2 | 0.90 | — | — | — | — | — | — | |
VDSR[ | 37.5 | 0.96 | 33.0 | 0.91 | 30.8 | 0.91 | 31.9 | 0.90 | — | — | 37.2 | 0.98 | |
RCAN[ | 38.3 | 0.96 | 34.2 | 0.92 | 33.5 | 0.94 | 37.2 | 0.97 | 39.6 | 0.98 | — | — | |
DRCN[ | 37.6 | 0.96 | 33.0 | 0.91 | 30.8 | 0.91 | — | — | — | — | — | — | |
RDN[ | 38.3 | 0.96 | 34.1 | 0.92 | 33.1 | 0.94 | 27.7 | 0.74 | 39.4 | 0.98 | — | — | |
DRRN[ | 37.7 | 0.96 | 33.2 | 0.91 | 31.2 | 0.92 | 32.1 | 0.90 | — | — | — | — | |
SRFBN[ | 38.2 | 0.96 | 33.9 | 0.92 | 32.8 | 0.93 | — | — | 39.3 | 0.98 | — | — | |
SRGAN[ | 38.9 | 0.97 | 35.6 | 0.94 | 35.2 | 0.96 | 33.8 | 0.93 | 38.3 | 0.96 | 34.4 | 0.97 | |
EDSR[ | 38.2 | 0.96 | 34.0 | 0.92 | 33.1 | 0.94 | 27.7 | 0.74 | 39.1 | 0.98 | 35.1 | 0.97 | |
ESRGAN[ | 38.2 | 0.96 | 34.0 | 0.92 | 33.1 | 0.94 | 32.4 | 0.90 | — | — | 36.1 | 0.96 | |
SMSR[ | 38.0 | 0.96 | 33.6 | 0.91 | 32.2 | 0.93 | 32.2 | 0.90 | 38.8 | 0.98 | — | — | |
SRFeat[ | 32.3 | 0.89 | 28.7 | 0.78 | — | — | 27.6 | 0.74 | — | — | — | — | |
IRN[ | 44.0 | 0.98 | 40.8 | 0.97 | 39.9 | 0.98 | 41.3 | 0.98 | — | — | 44.3 | 0.99 | |
HAN[ | 38.3 | 0.96 | 34.2 | 0.92 | 33.5 | 0.93 | 32.5 | 0.90 | 39.6 | 0.98 | — | — | |
Meta-SR[ | 34.0 | 0.92 | 32.3 | 0.90 | — | — | — | — | 39.2 | 0.98 | 35.2 | 0.95 | |
SRCNN[ | | 32.8 | 0.91 | 29.3 | 0.82 | 26.2 | 0.80 | 28.4 | 0.79 | 30.5 | 0.91 | — | — |
FSRCNN[ | 33.2 | 0.91 | 29.4 | 0.82 | 26.4 | 0.81 | 28.5 | 0.79 | 31.1 | 0.92 | — | — | |
FSRCNN*[ | 33.3 | 0.92 | 29.6 | 0.83 | 26.7 | 0.82 | — | — | — | — | — | — | |
VDSR[ | 33.7 | 0.92 | 29.8 | 0.83 | 27.1 | 0.83 | 36.7 | 0.97 | 32.0 | 0.93 | — | — | |
RCAN[ | 34.9 | 0.93 | 30.8 | 0.85 | 29.3 | 0.87 | 29.3 | 0.81 | 34.8 | 0.95 | — | — | |
DRCN[ | 33.8 | 0.90 | 29.8 | 0.83 | 27.2 | 0.83 | — | — | — | — | — | — | |
RDN[ | 34.9 | 0.93 | 30.7 | 0.85 | 29.0 | 0.87 | 27.7 | 0.74 | 33.4 | 0.94 | — | — | |
DRRN[ | 34.0 | 0.92 | 30.0 | 0.83 | 27.5 | 0.84 | 29.0 | 0.80 | — | — | — | — | |
SRFBN[ | 34.8 | 0.93 | 30.6 | 0.85 | 28.9 | 0.87 | — | — | 34.4 | 0.95 | — | — | |
SRGAN[ | 33.7 | 0.93 | 30.2 | 0.87 | 26.9 | 0.84 | 29.6 | 0.84 | 31.0 | 0.94 | 30.9 | 0.93 | |
EDSR[ | 34.8 | 0.93 | 30.7 | 0.85 | 29.0 | 0.87 | 29.3 | 0.81 | 34.1 | 0.95 | 31.4 | 0.93 | |
ESRGAN[ | 36.2 | 0.95 | 32.7 | 0.90 | 31.4 | 0.92 | 31.6 | 0.88 | — | — | 35.1 | 0.93 | |
SMSR[ | 34.4 | 0.93 | 30.3 | 0.84 | 28.3 | 0.86 | 29.1 | 0.81 | 33.7 | 0.94 | — | — | |
HAN[ | 34.9 | 0.93 | 30.8 | 0.85 | 29.3 | 0.87 | 29.4 | 0.81 | 34.8 | 0.95 | — | — | |
Meta-SR[ | 30.6 | 0.85 | 29.3 | 0.81 | — | — | — | — | 34.1 | 0.95 | 31.4 | 0.89 | |
SRCNN[ | | 30.5 | 0.86 | 27.5 | 0.75 | 24.5 | 0.72 | 26.9 | 0.71 | 27.6 | 0.86 | — | — |
FSRCNN[ | 30.7 | 0.87 | 27.6 | 0.76 | 24.6 | 0.73 | 27.0 | 0.72 | 27.9 | 0.86 | — | — | |
FSRCNN*[ | 31.0 | 0.88 | 27.8 | 0.76 | 27.1 | 0.72 | — | — | — | — | — | — | |
VDSR[ | 31.4 | 0.88 | 28.0 | 0.77 | 25.2 | 0.75 | 27.9 | 0.86 | 28.8 | 0.89 | — | — | |
RCAN[ | 32.7 | 0.90 | 29.0 | 0.79 | 27.1 | 0.81 | 28.8 | 0.89 | 31.7 | 0.92 | — | — | |
DRCN[ | 31.5 | 0.89 | 28.0 | 0.77 | 25.1 | 0.75 | — | — | — | — | — | — | |
RDN[ | 32.6 | 0.90 | 28.9 | 0.79 | 26.8 | 0.81 | 27.7 | 0.74 | 31.4 | 0.92 | — | — | |
DRRN[ | 31.7 | 0.89 | 28.2 | 0.77 | 25.4 | 0.76 | 27.4 | 0.73 | — | — | — | — | |
SRFBN[ | 32.6 | 0.90 | 28.9 | 0.79 | 26.7 | 0.80 | — | — | 31.4 | 0.92 | — | — | |
SRGAN[ | 33.9 | 0.91 | 30.3 | 0.84 | 29.3 | 0.87 | 29.2 | 0.80 | 32.8 | 0.88 | 28.9 | 0.90 | |
EDSR[ | 32.5 | 0.90 | 28.8 | 0.79 | 26.6 | 0.80 | 27.7 | 0.74 | 31.0 | 0.91 | 29.4 | 0.90 | |
ESRGAN[ | 32.7 | 0.90 | 29.0 | 0.79 | 27.8 | 0.75 | 27.0 | 0.82 | — | — | 30.9 | 0.85 | |
NatSR[ | 31.0 | 0.86 | 27.4 | 0.73 | 25.5 | 0.76 | 26.4 | 0.68 | — | — | — | — | |
SMSR[ | 32.1 | 0.89 | 28.6 | 0.78 | 26.1 | 0.79 | 27.6 | 0.74 | 30.5 | 0.91 | — | — | |
SRFeat[ | 32.3 | 0.89 | 28.7 | 0.78 | — | — | 27.6 | 0.74 | — | — | — | — | |
IRN[ | 36.2 | 0.95 | 32.7 | 0.90 | 31.4 | 0.92 | 31.6 | 0.88 | — | — | 35.1 | 0.93 | |
HAN[ | 32.8 | 0.90 | 29.0 | 0.79 | 27.0 | 0.81 | 27.9 | 0.75 | 31.7 | 0.92 | — | — | |
Meta-SR[ | 28.8 | 0.79 | 27.8 | 0.74 | — | — | — | — | 31.0 | 0.92 | 29.4 | 0.85 |
表7 深度学习后各算法的重建效果对比
Table 7 Comparison of reconstruction effects of each algorithm after deep learning
算法 | 大小 | Set5 | Set14 | Urban100 | BSD100 | Manga109 | DIV2K | ||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | PSNR/dB | SSIM | ||
SRCNN[ | | 36.7 | 0.95 | 32.5 | 0.91 | 29.5 | 0.89 | 31.4 | 0.89 | 35.6 | 0.97 | — | — |
FSRCNN[ | 37.1 | 0.96 | 32.7 | 0.91 | 29.9 | 0.90 | 31.5 | 0.89 | 36.7 | 0.97 | — | — | |
FSRCNN*[ | 37.3 | 0.96 | 32.8 | 0.91 | 30.2 | 0.90 | — | — | — | — | — | — | |
VDSR[ | 37.5 | 0.96 | 33.0 | 0.91 | 30.8 | 0.91 | 31.9 | 0.90 | — | — | 37.2 | 0.98 | |
RCAN[ | 38.3 | 0.96 | 34.2 | 0.92 | 33.5 | 0.94 | 37.2 | 0.97 | 39.6 | 0.98 | — | — | |
DRCN[ | 37.6 | 0.96 | 33.0 | 0.91 | 30.8 | 0.91 | — | — | — | — | — | — | |
RDN[ | 38.3 | 0.96 | 34.1 | 0.92 | 33.1 | 0.94 | 27.7 | 0.74 | 39.4 | 0.98 | — | — | |
DRRN[ | 37.7 | 0.96 | 33.2 | 0.91 | 31.2 | 0.92 | 32.1 | 0.90 | — | — | — | — | |
SRFBN[ | 38.2 | 0.96 | 33.9 | 0.92 | 32.8 | 0.93 | — | — | 39.3 | 0.98 | — | — | |
SRGAN[ | 38.9 | 0.97 | 35.6 | 0.94 | 35.2 | 0.96 | 33.8 | 0.93 | 38.3 | 0.96 | 34.4 | 0.97 | |
EDSR[ | 38.2 | 0.96 | 34.0 | 0.92 | 33.1 | 0.94 | 27.7 | 0.74 | 39.1 | 0.98 | 35.1 | 0.97 | |
ESRGAN[ | 38.2 | 0.96 | 34.0 | 0.92 | 33.1 | 0.94 | 32.4 | 0.90 | — | — | 36.1 | 0.96 | |
SMSR[ | 38.0 | 0.96 | 33.6 | 0.91 | 32.2 | 0.93 | 32.2 | 0.90 | 38.8 | 0.98 | — | — | |
SRFeat[ | 32.3 | 0.89 | 28.7 | 0.78 | — | — | 27.6 | 0.74 | — | — | — | — | |
IRN[ | 44.0 | 0.98 | 40.8 | 0.97 | 39.9 | 0.98 | 41.3 | 0.98 | — | — | 44.3 | 0.99 | |
HAN[ | 38.3 | 0.96 | 34.2 | 0.92 | 33.5 | 0.93 | 32.5 | 0.90 | 39.6 | 0.98 | — | — | |
Meta-SR[ | 34.0 | 0.92 | 32.3 | 0.90 | — | — | — | — | 39.2 | 0.98 | 35.2 | 0.95 | |
SRCNN[ | | 32.8 | 0.91 | 29.3 | 0.82 | 26.2 | 0.80 | 28.4 | 0.79 | 30.5 | 0.91 | — | — |
FSRCNN[ | 33.2 | 0.91 | 29.4 | 0.82 | 26.4 | 0.81 | 28.5 | 0.79 | 31.1 | 0.92 | — | — | |
FSRCNN*[ | 33.3 | 0.92 | 29.6 | 0.83 | 26.7 | 0.82 | — | — | — | — | — | — | |
VDSR[ | 33.7 | 0.92 | 29.8 | 0.83 | 27.1 | 0.83 | 36.7 | 0.97 | 32.0 | 0.93 | — | — | |
RCAN[ | 34.9 | 0.93 | 30.8 | 0.85 | 29.3 | 0.87 | 29.3 | 0.81 | 34.8 | 0.95 | — | — | |
DRCN[ | 33.8 | 0.90 | 29.8 | 0.83 | 27.2 | 0.83 | — | — | — | — | — | — | |
RDN[ | 34.9 | 0.93 | 30.7 | 0.85 | 29.0 | 0.87 | 27.7 | 0.74 | 33.4 | 0.94 | — | — | |
DRRN[ | 34.0 | 0.92 | 30.0 | 0.83 | 27.5 | 0.84 | 29.0 | 0.80 | — | — | — | — | |
SRFBN[ | 34.8 | 0.93 | 30.6 | 0.85 | 28.9 | 0.87 | — | — | 34.4 | 0.95 | — | — | |
SRGAN[ | 33.7 | 0.93 | 30.2 | 0.87 | 26.9 | 0.84 | 29.6 | 0.84 | 31.0 | 0.94 | 30.9 | 0.93 | |
EDSR[ | 34.8 | 0.93 | 30.7 | 0.85 | 29.0 | 0.87 | 29.3 | 0.81 | 34.1 | 0.95 | 31.4 | 0.93 | |
ESRGAN[ | 36.2 | 0.95 | 32.7 | 0.90 | 31.4 | 0.92 | 31.6 | 0.88 | — | — | 35.1 | 0.93 | |
SMSR[ | 34.4 | 0.93 | 30.3 | 0.84 | 28.3 | 0.86 | 29.1 | 0.81 | 33.7 | 0.94 | — | — | |
HAN[ | 34.9 | 0.93 | 30.8 | 0.85 | 29.3 | 0.87 | 29.4 | 0.81 | 34.8 | 0.95 | — | — | |
Meta-SR[ | 30.6 | 0.85 | 29.3 | 0.81 | — | — | — | — | 34.1 | 0.95 | 31.4 | 0.89 | |
SRCNN[ | | 30.5 | 0.86 | 27.5 | 0.75 | 24.5 | 0.72 | 26.9 | 0.71 | 27.6 | 0.86 | — | — |
FSRCNN[ | 30.7 | 0.87 | 27.6 | 0.76 | 24.6 | 0.73 | 27.0 | 0.72 | 27.9 | 0.86 | — | — | |
FSRCNN*[ | 31.0 | 0.88 | 27.8 | 0.76 | 27.1 | 0.72 | — | — | — | — | — | — | |
VDSR[ | 31.4 | 0.88 | 28.0 | 0.77 | 25.2 | 0.75 | 27.9 | 0.86 | 28.8 | 0.89 | — | — | |
RCAN[ | 32.7 | 0.90 | 29.0 | 0.79 | 27.1 | 0.81 | 28.8 | 0.89 | 31.7 | 0.92 | — | — | |
DRCN[ | 31.5 | 0.89 | 28.0 | 0.77 | 25.1 | 0.75 | — | — | — | — | — | — | |
RDN[ | 32.6 | 0.90 | 28.9 | 0.79 | 26.8 | 0.81 | 27.7 | 0.74 | 31.4 | 0.92 | — | — | |
DRRN[ | 31.7 | 0.89 | 28.2 | 0.77 | 25.4 | 0.76 | 27.4 | 0.73 | — | — | — | — | |
SRFBN[ | 32.6 | 0.90 | 28.9 | 0.79 | 26.7 | 0.80 | — | — | 31.4 | 0.92 | — | — | |
SRGAN[ | 33.9 | 0.91 | 30.3 | 0.84 | 29.3 | 0.87 | 29.2 | 0.80 | 32.8 | 0.88 | 28.9 | 0.90 | |
EDSR[ | 32.5 | 0.90 | 28.8 | 0.79 | 26.6 | 0.80 | 27.7 | 0.74 | 31.0 | 0.91 | 29.4 | 0.90 | |
ESRGAN[ | 32.7 | 0.90 | 29.0 | 0.79 | 27.8 | 0.75 | 27.0 | 0.82 | — | — | 30.9 | 0.85 | |
NatSR[ | 31.0 | 0.86 | 27.4 | 0.73 | 25.5 | 0.76 | 26.4 | 0.68 | — | — | — | — | |
SMSR[ | 32.1 | 0.89 | 28.6 | 0.78 | 26.1 | 0.79 | 27.6 | 0.74 | 30.5 | 0.91 | — | — | |
SRFeat[ | 32.3 | 0.89 | 28.7 | 0.78 | — | — | 27.6 | 0.74 | — | — | — | — | |
IRN[ | 36.2 | 0.95 | 32.7 | 0.90 | 31.4 | 0.92 | 31.6 | 0.88 | — | — | 35.1 | 0.93 | |
HAN[ | 32.8 | 0.90 | 29.0 | 0.79 | 27.0 | 0.81 | 27.9 | 0.75 | 31.7 | 0.92 | — | — | |
Meta-SR[ | 28.8 | 0.79 | 27.8 | 0.74 | — | — | — | — | 31.0 | 0.92 | 29.4 | 0.85 |
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