Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (5): 972-990.DOI: 10.3778/j.issn.1673-9418.2111126
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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:
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:
CLC Number:
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.
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.
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URL: http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2111126
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 |
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 |
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[ |
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 | — | √ | √ | — | — | |
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 |
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 |
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 |
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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