电子学报 ›› 2022, Vol. 50 ›› Issue (9): 2265-2294.DOI: 10.12263/DZXB.20220091
吴靖1,2, 叶晓晶1,2, 黄峰1,2, 陈丽琼1,2, 王志锋1,2, 刘文犀2,3
收稿日期:
2022-01-17
修回日期:
2022-05-13
出版日期:
2022-09-25
作者简介:
基金资助:
WU Jing1,2, YE Xiao-jing1,2, HUANG Feng1,2, CHEN Li-qiong1,2, WANG Zhi-feng1,2, LIU Wen-xi2,3
Received:
2022-01-17
Revised:
2022-05-13
Online:
2022-09-25
Published:
2022-10-26
摘要:
图像超分辨率重建是计算机视觉中的基本图像处理技术之一,不仅可以提高图像分辨率改善图像质量,还可以辅助其他计算机视觉任务. 近年来,随着人工智能浪潮的兴起,基于深度学习的图像超分辨率重建也取得了显著进展. 本文在简述图像超分辨率重建方法的基础上,全面综述了基于深度学习的单帧图像超分辨率重建的技术架构及研究历程,包括数据集构建方式、网络模型基本框架以及用于图像质量评估的主、客观评价指标,重点介绍了根据网络结构及图像重建效果划分的基于卷积神经网络的方法、基于生成对抗网络的方法以及基于Transformer的方法,并对相关网络模型加以评述和对比,最后依据网络模型和超分辨率重建挑战赛相关内容,展望了图像超分辨率重建未来的发展趋势.
中图分类号:
吴靖, 叶晓晶, 黄峰, 陈丽琼, 王志锋, 刘文犀. 基于深度学习的单帧图像超分辨率重建综述[J]. 电子学报, 2022, 50(9): 2265-2294.
WU Jing, YE Xiao-jing, HUANG Feng, CHEN Li-qiong, WANG Zhi-feng, LIU Wen-xi. A Review of Single Image Super-Resolution Reconstruction Based on Deep Learning[J]. Acta Electronica Sinica, 2022, 50(9): 2265-2294.
数据集名称 | 图像 数量/张 | 合成/真实数据集 (训练集/验证集/测试集) | 图像格式 | 图像内容 |
---|---|---|---|---|
T91 | 91 | 合成数据集(训练集) | PNG | 包括动植物、人、车等局部纹理图像 |
Timofte | 110 | 合成数据集(训练集) | PNG | 包括T91, Set5和Set14三个数据集的图像 |
291-images | 291 | 合成数据集(训练集) | PNG, JPG | 包括T91和BSD两个数据集的图像 |
General-100 | 100 | 合成数据集(训练集) | BMP | 包括建筑纹理、动植物、人、食物、日用品等图像 |
MSCOCO | 328 000 | 合成数据集(训练集) | JPG | 包括91种易识别的物体类型在自然环境中的复杂日常场景的图像 |
ImageNet | 3 200 000 | 合成数据集(训练集) | JPG | 包括动植物、交通工具、家具、乐器、地质构造、工具等图像 |
DIV2K | 1 000 | 合成数据集(800张训练集、100张验证集、100张测试集) | PNG | 包括人、手工艺品、环境(城市、村庄)、动植物、自然风景等图像 |
Flickr2K | 2 650 | 合成数据集(训练集) | PNG | 包括人、车、动植物、食物、建筑和风景等图像 |
DF2K | 3 450 | 合成数据集(训练集) | PNG | 包括DIV2K和Flickr2K两个数据集的图像 |
DPED | 5 827 | 真实数据集(5 614张训练集、113张验证集、100张测试集) | PNG | 包括各种道路交通场景(建筑、植物、道路等)等图像 |
OutdoorScene(OST) | 10 624 | 合成数据集(10 324张训练集、300张测试集,即OST300) | PNG | 包括动物、建筑、草、山、植物、天空和水等7类纹理丰富的图像 |
DIV8K | 1 504 | 真实数据集(1 304张训练集、100张验证集、100张测试集) | PNG | 包括人、动物、建筑和风景等各种场景和物体的图像 |
RealSR | 595 | 真实数据集(550张训练集、45张测试集) | PNG | 包括建筑、风景、动植物、海报、物体等纹理丰富的室内外场景图像 |
DRealSR | 884(×2) 783(×3) 840(×4) | 真实数据集(对于尺度因子2, 3, 4分别用83, 84, 93张图像进行测试,其余用于训练) | PNG | 包括广告海报、植物、办公室、建筑物等室内外场景的图像 |
Set5 | 5 | 合成数据集(测试集) | PNG | 包括婴儿、鸟、蝴蝶、头部和女士等5张图像 |
Set14 | 14 | 合成数据集(测试集) | PNG | 包括动植物、风景(船和桥)、PPT和人等图像 |
Berkeley Segmentation Dataset(BSD)/BSD500 | 500 | 合成数据集(测试集) | JPG | 包括动物、建筑、食物、风景、人和植物等图像.BSD100和BSD300分别是BSD500中常用的100或300张图片 |
Urban100 | 100 | 合成数据集(测试集) | PNG | 包括不同类型的建筑图像 |
Manga109 | 109 | 合成数据集(测试集) | PNG | 包括来自日本漫画书的图像 |
PIRM | 200 | 合成数据集(100张验证集、100张测试集) | PNG | 包括人、物、环境、植物、自然风景等图像 |
DIV2KRK | 100 | 合成数据集(测试集) | PNG | 包括对DIV2K验证集的100张图像进行更复杂退化操作的图像 |
DIV2K4D | 400 | 合成数据集(测试集) | PNG | 包括对100张DIV2K验证集采取四种退化的图像 |
表1 图像超分辨率重建常用数据集概述
数据集名称 | 图像 数量/张 | 合成/真实数据集 (训练集/验证集/测试集) | 图像格式 | 图像内容 |
---|---|---|---|---|
T91 | 91 | 合成数据集(训练集) | PNG | 包括动植物、人、车等局部纹理图像 |
Timofte | 110 | 合成数据集(训练集) | PNG | 包括T91, Set5和Set14三个数据集的图像 |
291-images | 291 | 合成数据集(训练集) | PNG, JPG | 包括T91和BSD两个数据集的图像 |
General-100 | 100 | 合成数据集(训练集) | BMP | 包括建筑纹理、动植物、人、食物、日用品等图像 |
MSCOCO | 328 000 | 合成数据集(训练集) | JPG | 包括91种易识别的物体类型在自然环境中的复杂日常场景的图像 |
ImageNet | 3 200 000 | 合成数据集(训练集) | JPG | 包括动植物、交通工具、家具、乐器、地质构造、工具等图像 |
DIV2K | 1 000 | 合成数据集(800张训练集、100张验证集、100张测试集) | PNG | 包括人、手工艺品、环境(城市、村庄)、动植物、自然风景等图像 |
Flickr2K | 2 650 | 合成数据集(训练集) | PNG | 包括人、车、动植物、食物、建筑和风景等图像 |
DF2K | 3 450 | 合成数据集(训练集) | PNG | 包括DIV2K和Flickr2K两个数据集的图像 |
DPED | 5 827 | 真实数据集(5 614张训练集、113张验证集、100张测试集) | PNG | 包括各种道路交通场景(建筑、植物、道路等)等图像 |
OutdoorScene(OST) | 10 624 | 合成数据集(10 324张训练集、300张测试集,即OST300) | PNG | 包括动物、建筑、草、山、植物、天空和水等7类纹理丰富的图像 |
DIV8K | 1 504 | 真实数据集(1 304张训练集、100张验证集、100张测试集) | PNG | 包括人、动物、建筑和风景等各种场景和物体的图像 |
RealSR | 595 | 真实数据集(550张训练集、45张测试集) | PNG | 包括建筑、风景、动植物、海报、物体等纹理丰富的室内外场景图像 |
DRealSR | 884(×2) 783(×3) 840(×4) | 真实数据集(对于尺度因子2, 3, 4分别用83, 84, 93张图像进行测试,其余用于训练) | PNG | 包括广告海报、植物、办公室、建筑物等室内外场景的图像 |
Set5 | 5 | 合成数据集(测试集) | PNG | 包括婴儿、鸟、蝴蝶、头部和女士等5张图像 |
Set14 | 14 | 合成数据集(测试集) | PNG | 包括动植物、风景(船和桥)、PPT和人等图像 |
Berkeley Segmentation Dataset(BSD)/BSD500 | 500 | 合成数据集(测试集) | JPG | 包括动物、建筑、食物、风景、人和植物等图像.BSD100和BSD300分别是BSD500中常用的100或300张图片 |
Urban100 | 100 | 合成数据集(测试集) | PNG | 包括不同类型的建筑图像 |
Manga109 | 109 | 合成数据集(测试集) | PNG | 包括来自日本漫画书的图像 |
PIRM | 200 | 合成数据集(100张验证集、100张测试集) | PNG | 包括人、物、环境、植物、自然风景等图像 |
DIV2KRK | 100 | 合成数据集(测试集) | PNG | 包括对DIV2K验证集的100张图像进行更复杂退化操作的图像 |
DIV2K4D | 400 | 合成数据集(测试集) | PNG | 包括对100张DIV2K验证集采取四种退化的图像 |
级别 | 绝对主观评价指标 | 相对主观评价指标 |
---|---|---|
1 | 很好(第1名) | 一组中最好(5分) |
2 | 较好(第2名) | 比该组的平均水平好(4分) |
3 | 一般(第3名) | 该组中平均水平(3分) |
4 | 较差(第4名) | 比该组的平均水平差(2分) |
5 | 很差(第5名) | 一组中最差(1分) |
表2 主观评价指标的评价尺度
级别 | 绝对主观评价指标 | 相对主观评价指标 |
---|---|---|
1 | 很好(第1名) | 一组中最好(5分) |
2 | 较好(第2名) | 比该组的平均水平好(4分) |
3 | 一般(第3名) | 该组中平均水平(3分) |
4 | 较差(第4名) | 比该组的平均水平差(2分) |
5 | 很差(第5名) | 一组中最差(1分) |
网络名称-- 发表时间(类型) | 网络框架 (上采样方法) | 训练集 | LR图像获取 方式 | 数据 增强 | 测试集 | 损失 函数 | 评价指标 |
---|---|---|---|---|---|---|---|
SRCNN--2014 (基于S-CNN) | 预上采样 (双三次插值) | 91-images, ImageNet | Bicubic+GB | — | Set5, Set14 | L2 | PSNR, SSIM, runtime |
FSRCNN--2016 (基于S-CNN) | 后上采样 (反卷积) | 91-images, General-100 | 下采样 | 缩放、旋转 | Set5, Set14, BSD200 | L2 | PSNR, SSIM, IFC, runtime |
ESPCN--2016 (基于S-CNN) | 后上采样 (亚像素卷积) | ImageNet | 下采样+GB | — | 91-images, Set5, Set14, BSD300, BSD500, super texture | L2 | PSNR, runtime |
VDSR--2016 (基于ResNet) | 预上采样 (双三次插值) | 291-images | 下采样 | 旋转、翻转 | Set5, Set14, Urban100, B100 | L2 | PSNR, SSIM, runtime |
RED-Net--2016 (基于ResNet) | 预上采样 (双三次插值) | BSD300 | 下采样 | 旋转、翻转 | Set5, Set14, BSD100 | L2 | PSNR, SSIM |
DRCN--2016 (基于RNN) | 预上采样 (双三次插值) | 91-images | 下采样 | — | Set5, Set14, B100, Urban100 | L2 | PSNR, SSIM |
LapSRN--2017 (基于ResNet) | 渐进式上采样 (转置卷积) | 291-images | Bicubic | 缩放、旋转、翻转 | Set5, Set14, BSDS100, Urban100, Manga109 | LCharbonnier | PSNR, SSIM, IFC |
EDSR--2017 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 翻转、旋转 | Set5, Set14, B100, Urban100, DIV2K | L1 | PSNR, SSIM |
DRRN--2017 (基于RNN) | 预上采样 (双三次插值) | 291-images | 下采样 | 翻转、旋转 | Set5, Set14, BSD100, Urban100 | L2 | PSNR, SSIM, IFC |
SRDenseNet--2017 (基于DenseNet) | 后上采样 (反卷积) | ImageNet | Bicubic | — | Set5, Set14, B100, Urban100 | L2 | PSNR, SSIM |
MemNet--2017 (基于DenseNet) | 预上采样 (双三次插值) | 291-images | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100 | L2 | PSNR, SSIM |
SRGAN--2017 (基于GAN) | 后上采样 (亚像素卷积) | ImageNet | Bicubic | — | Set5, Set14, BSD100 | Lpercep, LGAN | PSNR, SSIM, MOS |
EnhanceNet--2017 (基于GAN) | 后上采样 (最近邻插值) | MSCOCO | Bicubic | — | Set5, Set14, BSD100, Urban100 | Lpercep, LGAN, Ltexture | PSNR, SSIM, IFC, MOS |
DBPN--2018 (基于ResNet) | 迭代式上下采样(反卷积) | DIV2K, Flickr2K, ImageNet | Bicubic | — | Set5, Set14, BSDS100, Urban100, Manga109 | L2 | PSNR,SSIM |
MSRN--2018 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 缩放、旋转、翻转 | Set5, Set14, BSDS100, Urban100, Manga109 | L1 | PSNR, SSIM |
RCAN--2018 (基于AM) | 后上采样 (亚像素卷积) | DIV2K | ①Bicubic ②模糊下采样 | 旋转、翻转 | Set5, Set14, B100, Urban100, Manga109 | L1 | PSNR, SSIM |
ESRGAN--2018 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flickr2K, OST | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100, PIRM | Lpercep, LGAN, L1 | PSNR, PI |
LP-KPN--2019 (基于ResNet) | 渐进式上采 (shuffle upsample) | RealSR | — | 旋转、翻转 | RealSR | L2 | PSNR, SSIM |
SRFBN--2019 (基于RNN) | 后上采样 (反卷积) | DIV2K, Flickr2K | ①Bicubic ②下采样+GB ③Bicubic+GN | 翻转、旋转 | Set5, Set14, B100, Urban100, Manga109 | L1 | PSNR, SSIM |
Meta-SR--2019 (基于DenseNet) | 后上采样 (元上采样) | DIV2K | 下采样 | 翻转、旋转 | Set14, B100, Manga109, DIV2K | L1 | PSNR, SSIM |
SAN--2019 (基于AM) | 后上采样 (亚像素卷积) | DIV2K | ①Bicubic ②模糊下采样 | 旋转、翻转 | Set5, Set14, BSD100, Urban100, Manga109 | L1 | PSNR, SSIM |
DAN--2020 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K, Flickr2K | ①GB+下采样 ②GB(旋转、噪声、归一化)+ 下采样 | — | ①Set5, Set14, Urban100, BSD100, Manga109 ②DIV2KRK | L1 | PSNR, SSIM |
HDRN--2020 (基于DenseNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100, Manga109, Jilin-1 satellite imagery, Kaggle | LCharbonnier | PSNR, SSIM |
CDC-2020 (基于AM) | 渐进式上采样 (亚像素卷积) | RealSR, DRealSR | — | — | RealSR, DRealSR | LGW(L1) | PSNR, SSIM, LPIPS |
RealSR--2020 (无监督式) | 后上采样 (最近邻插值) | ①DF2K ②DPED | 无监督式 退化模型 | — | ①DF2K ②DPED | L1, Lpercep, LGAN | ①PSNR, SSIM, LPIPS ②MOR |
RFB-ESRGAN--2020 (基于GAN) | 后上采样 (交替使用最近邻插值和亚像 素卷积) | DIV8K, DIV2K, Flickr2K, OST | Bicubic | 翻转、旋转 | DIV8K | L1, LVGG, LGAN | PSNR, SSIM, LPIPS, PI |
CRN--2021 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | — | Set5, Set14, B100, Urban100 | L1 | PSNR, SSIM |
SMSR--2021 (基于LN) | 后上采样 (亚像素卷积+双三次插值) | DIV2K | 下采样 | 旋转、翻转 | Set5, Set14, B100, Urban100, Manga109 | L1, Lreg | PSNR, SSIM, Params, FLOPs |
BSRGAN--2021 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flick2K,WED, FFHQ | B+下采样+N | — | ①合成的DIV2K4D, ②真实的RealSRSet | L1, Lpercep,LPatchGAN | ①PSNR, LPIPS ②NIQE, NRQM, PI |
Real-ESRGAN--2021 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flickr2K, OST | Bicubic+B+N+J | — | RealSR, DRealSR, OST300, DPED, ADE20K | L1, Lpercep, LGAN | 视觉效果 |
IPT--2021 (基于Transformer) | — | ImageNet | Bicubic | — | Set5, Set14, B100, Urban100 | L1, Lcontrastive | PSNR |
SwinIR--2021 (基于Transformer) | 后上采样 (亚像素卷积) | DIV2K, Flickr2K | ①Bicubic ②B+下采样+N | — | Set5, Set14, Urban100, BSD100, Manga109 | ①L1 ②L1, LGAN, Lpercep | PSNR, SSIM, Params, Mult-Adds |
表3 基于深度学习的单帧图像超分辨率重建典型网络模型总结
网络名称-- 发表时间(类型) | 网络框架 (上采样方法) | 训练集 | LR图像获取 方式 | 数据 增强 | 测试集 | 损失 函数 | 评价指标 |
---|---|---|---|---|---|---|---|
SRCNN--2014 (基于S-CNN) | 预上采样 (双三次插值) | 91-images, ImageNet | Bicubic+GB | — | Set5, Set14 | L2 | PSNR, SSIM, runtime |
FSRCNN--2016 (基于S-CNN) | 后上采样 (反卷积) | 91-images, General-100 | 下采样 | 缩放、旋转 | Set5, Set14, BSD200 | L2 | PSNR, SSIM, IFC, runtime |
ESPCN--2016 (基于S-CNN) | 后上采样 (亚像素卷积) | ImageNet | 下采样+GB | — | 91-images, Set5, Set14, BSD300, BSD500, super texture | L2 | PSNR, runtime |
VDSR--2016 (基于ResNet) | 预上采样 (双三次插值) | 291-images | 下采样 | 旋转、翻转 | Set5, Set14, Urban100, B100 | L2 | PSNR, SSIM, runtime |
RED-Net--2016 (基于ResNet) | 预上采样 (双三次插值) | BSD300 | 下采样 | 旋转、翻转 | Set5, Set14, BSD100 | L2 | PSNR, SSIM |
DRCN--2016 (基于RNN) | 预上采样 (双三次插值) | 91-images | 下采样 | — | Set5, Set14, B100, Urban100 | L2 | PSNR, SSIM |
LapSRN--2017 (基于ResNet) | 渐进式上采样 (转置卷积) | 291-images | Bicubic | 缩放、旋转、翻转 | Set5, Set14, BSDS100, Urban100, Manga109 | LCharbonnier | PSNR, SSIM, IFC |
EDSR--2017 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 翻转、旋转 | Set5, Set14, B100, Urban100, DIV2K | L1 | PSNR, SSIM |
DRRN--2017 (基于RNN) | 预上采样 (双三次插值) | 291-images | 下采样 | 翻转、旋转 | Set5, Set14, BSD100, Urban100 | L2 | PSNR, SSIM, IFC |
SRDenseNet--2017 (基于DenseNet) | 后上采样 (反卷积) | ImageNet | Bicubic | — | Set5, Set14, B100, Urban100 | L2 | PSNR, SSIM |
MemNet--2017 (基于DenseNet) | 预上采样 (双三次插值) | 291-images | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100 | L2 | PSNR, SSIM |
SRGAN--2017 (基于GAN) | 后上采样 (亚像素卷积) | ImageNet | Bicubic | — | Set5, Set14, BSD100 | Lpercep, LGAN | PSNR, SSIM, MOS |
EnhanceNet--2017 (基于GAN) | 后上采样 (最近邻插值) | MSCOCO | Bicubic | — | Set5, Set14, BSD100, Urban100 | Lpercep, LGAN, Ltexture | PSNR, SSIM, IFC, MOS |
DBPN--2018 (基于ResNet) | 迭代式上下采样(反卷积) | DIV2K, Flickr2K, ImageNet | Bicubic | — | Set5, Set14, BSDS100, Urban100, Manga109 | L2 | PSNR,SSIM |
MSRN--2018 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 缩放、旋转、翻转 | Set5, Set14, BSDS100, Urban100, Manga109 | L1 | PSNR, SSIM |
RCAN--2018 (基于AM) | 后上采样 (亚像素卷积) | DIV2K | ①Bicubic ②模糊下采样 | 旋转、翻转 | Set5, Set14, B100, Urban100, Manga109 | L1 | PSNR, SSIM |
ESRGAN--2018 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flickr2K, OST | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100, PIRM | Lpercep, LGAN, L1 | PSNR, PI |
LP-KPN--2019 (基于ResNet) | 渐进式上采 (shuffle upsample) | RealSR | — | 旋转、翻转 | RealSR | L2 | PSNR, SSIM |
SRFBN--2019 (基于RNN) | 后上采样 (反卷积) | DIV2K, Flickr2K | ①Bicubic ②下采样+GB ③Bicubic+GN | 翻转、旋转 | Set5, Set14, B100, Urban100, Manga109 | L1 | PSNR, SSIM |
Meta-SR--2019 (基于DenseNet) | 后上采样 (元上采样) | DIV2K | 下采样 | 翻转、旋转 | Set14, B100, Manga109, DIV2K | L1 | PSNR, SSIM |
SAN--2019 (基于AM) | 后上采样 (亚像素卷积) | DIV2K | ①Bicubic ②模糊下采样 | 旋转、翻转 | Set5, Set14, BSD100, Urban100, Manga109 | L1 | PSNR, SSIM |
DAN--2020 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K, Flickr2K | ①GB+下采样 ②GB(旋转、噪声、归一化)+ 下采样 | — | ①Set5, Set14, Urban100, BSD100, Manga109 ②DIV2KRK | L1 | PSNR, SSIM |
HDRN--2020 (基于DenseNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | 翻转、旋转 | Set5, Set14, BSD100, Urban100, Manga109, Jilin-1 satellite imagery, Kaggle | LCharbonnier | PSNR, SSIM |
CDC-2020 (基于AM) | 渐进式上采样 (亚像素卷积) | RealSR, DRealSR | — | — | RealSR, DRealSR | LGW(L1) | PSNR, SSIM, LPIPS |
RealSR--2020 (无监督式) | 后上采样 (最近邻插值) | ①DF2K ②DPED | 无监督式 退化模型 | — | ①DF2K ②DPED | L1, Lpercep, LGAN | ①PSNR, SSIM, LPIPS ②MOR |
RFB-ESRGAN--2020 (基于GAN) | 后上采样 (交替使用最近邻插值和亚像 素卷积) | DIV8K, DIV2K, Flickr2K, OST | Bicubic | 翻转、旋转 | DIV8K | L1, LVGG, LGAN | PSNR, SSIM, LPIPS, PI |
CRN--2021 (基于ResNet) | 后上采样 (亚像素卷积) | DIV2K | Bicubic | — | Set5, Set14, B100, Urban100 | L1 | PSNR, SSIM |
SMSR--2021 (基于LN) | 后上采样 (亚像素卷积+双三次插值) | DIV2K | 下采样 | 旋转、翻转 | Set5, Set14, B100, Urban100, Manga109 | L1, Lreg | PSNR, SSIM, Params, FLOPs |
BSRGAN--2021 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flick2K,WED, FFHQ | B+下采样+N | — | ①合成的DIV2K4D, ②真实的RealSRSet | L1, Lpercep,LPatchGAN | ①PSNR, LPIPS ②NIQE, NRQM, PI |
Real-ESRGAN--2021 (基于GAN) | 后上采样 (最近邻插值) | DIV2K, Flickr2K, OST | Bicubic+B+N+J | — | RealSR, DRealSR, OST300, DPED, ADE20K | L1, Lpercep, LGAN | 视觉效果 |
IPT--2021 (基于Transformer) | — | ImageNet | Bicubic | — | Set5, Set14, B100, Urban100 | L1, Lcontrastive | PSNR |
SwinIR--2021 (基于Transformer) | 后上采样 (亚像素卷积) | DIV2K, Flickr2K | ①Bicubic ②B+下采样+N | — | Set5, Set14, Urban100, BSD100, Manga109 | ①L1 ②L1, LGAN, Lpercep | PSNR, SSIM, Params, Mult-Adds |
挑战赛 | 赛道设置(尺度因子) | 数据集 | 评价指标 |
---|---|---|---|
NTIRE 2017--单图像超分辨率重建挑战 | 赛道1:经典的双三次下采样(×2、×3、×4) 赛道2:未知下采样(×2、×3、×4) | DIV2K 数据集 | PSNR, SSIM |
NTIRE 2018--单图像超分辨率重建挑战 | 赛道1:经典的双三次(×8) 赛道2:现实温和不利条件(×4) 赛道3:现实困难不利条件(×4) 赛道4:现实野生不利条件(×4) | DIV2K 数据集 | PSNR, SSIM |
PIRM 2018--感知图像超分辨率重建的挑战 | 赛道:采用双三次核下采样的单幅图像SR(×4) | PIRM数据集 | PI, RMSE |
NTIRE 2019--真实图像超分辨率重建挑战 | 赛道:真实世界SR(未知的尺度因子和退化算子) | RealSR 数据集 | PSNR, SSIM |
AIM 2019--真实世界图像超分辨率重建挑战 | 赛道1:源域(×4) 赛道2:目标域(×4) | DIV2K, Flickr2K 数据集 | PSNR, SSIM, LPIPS, MOS |
AIM 2019--受限超分辨率重建挑战 | 赛道1:参数(×4) 赛道2:推理时间(×4) 赛道3:保真度(PSNR)(×4) | DIV2K 数据集 | 参数个数、平均运行时间和平均PSNR |
AIM 2019--图像极端超分辨率重建挑战 | 赛道1:保真度(×16) 赛道2:感知质量(×16) | DIV8K 数据集 | 赛道1:PSNR, SSIM 赛道2:PSNR, SSIM, MOS |
NTIRE 2020--真实世界图像超分辨率重建挑战 | 赛道1:图像处理伪影(×4) 赛道2:智能手机图像(×4) | 赛道1:Flickr2K, DIV2K数据集 赛道2:DPED数据集 | 赛道1:PSNR, SSIM, LPIPS, MOS 赛道2:NIQE, BRISQUE, PIQE, NRQM, PI, IQA-Rank, MOR |
NTIRE 2020--感知极端超分辨率重建挑战 | 赛道:感知极端SR(×16) | DIV8K 数据集 | PSNR, SSIM, LPIPS, PI |
AIM 2020--真实图像超分辨率重建挑战 | 赛道1:上采样×2 赛道2:上采样×3 赛道3:上采样×4 | DRealSR 数据集 | PSNR, SSIM |
AIM 2020--高效超分辨率重建挑战 | 赛道:保持PSNR的同时,减少运行时间、参数、FLOPs、激活和内存消耗等一个或者几个方面(×4) | DIV2K 数据集 | 验证和测试PSNR、运行时间、参数数量、FLOPs数量、激活数量和推断期间消耗的最大GPU内存 |
NTIRE 2021--突发超分辨率重建挑战 | 赛道1:合成数据集(×4) 赛道2:真实世界数据集(×4) | 赛道1:合成突 发数据集 赛道2:BurstSR数据集 | 赛道1:PSNR, SSIM, LPIPS 赛道2:PSNR, SSIM, LPIPS, MOR |
Mobile AI 2021--基于移动NPU的实时量化图像超分辨率重建挑战 | 赛道:开发一个有效实际的解决方案用于移动任务 | DIV2K 数据集 | PSNR, SSIM, 运行时间 |
表4 图像超分辨率重建相关挑战赛内容总结
挑战赛 | 赛道设置(尺度因子) | 数据集 | 评价指标 |
---|---|---|---|
NTIRE 2017--单图像超分辨率重建挑战 | 赛道1:经典的双三次下采样(×2、×3、×4) 赛道2:未知下采样(×2、×3、×4) | DIV2K 数据集 | PSNR, SSIM |
NTIRE 2018--单图像超分辨率重建挑战 | 赛道1:经典的双三次(×8) 赛道2:现实温和不利条件(×4) 赛道3:现实困难不利条件(×4) 赛道4:现实野生不利条件(×4) | DIV2K 数据集 | PSNR, SSIM |
PIRM 2018--感知图像超分辨率重建的挑战 | 赛道:采用双三次核下采样的单幅图像SR(×4) | PIRM数据集 | PI, RMSE |
NTIRE 2019--真实图像超分辨率重建挑战 | 赛道:真实世界SR(未知的尺度因子和退化算子) | RealSR 数据集 | PSNR, SSIM |
AIM 2019--真实世界图像超分辨率重建挑战 | 赛道1:源域(×4) 赛道2:目标域(×4) | DIV2K, Flickr2K 数据集 | PSNR, SSIM, LPIPS, MOS |
AIM 2019--受限超分辨率重建挑战 | 赛道1:参数(×4) 赛道2:推理时间(×4) 赛道3:保真度(PSNR)(×4) | DIV2K 数据集 | 参数个数、平均运行时间和平均PSNR |
AIM 2019--图像极端超分辨率重建挑战 | 赛道1:保真度(×16) 赛道2:感知质量(×16) | DIV8K 数据集 | 赛道1:PSNR, SSIM 赛道2:PSNR, SSIM, MOS |
NTIRE 2020--真实世界图像超分辨率重建挑战 | 赛道1:图像处理伪影(×4) 赛道2:智能手机图像(×4) | 赛道1:Flickr2K, DIV2K数据集 赛道2:DPED数据集 | 赛道1:PSNR, SSIM, LPIPS, MOS 赛道2:NIQE, BRISQUE, PIQE, NRQM, PI, IQA-Rank, MOR |
NTIRE 2020--感知极端超分辨率重建挑战 | 赛道:感知极端SR(×16) | DIV8K 数据集 | PSNR, SSIM, LPIPS, PI |
AIM 2020--真实图像超分辨率重建挑战 | 赛道1:上采样×2 赛道2:上采样×3 赛道3:上采样×4 | DRealSR 数据集 | PSNR, SSIM |
AIM 2020--高效超分辨率重建挑战 | 赛道:保持PSNR的同时,减少运行时间、参数、FLOPs、激活和内存消耗等一个或者几个方面(×4) | DIV2K 数据集 | 验证和测试PSNR、运行时间、参数数量、FLOPs数量、激活数量和推断期间消耗的最大GPU内存 |
NTIRE 2021--突发超分辨率重建挑战 | 赛道1:合成数据集(×4) 赛道2:真实世界数据集(×4) | 赛道1:合成突 发数据集 赛道2:BurstSR数据集 | 赛道1:PSNR, SSIM, LPIPS 赛道2:PSNR, SSIM, LPIPS, MOR |
Mobile AI 2021--基于移动NPU的实时量化图像超分辨率重建挑战 | 赛道:开发一个有效实际的解决方案用于移动任务 | DIV2K 数据集 | PSNR, SSIM, 运行时间 |
1 | DAI D X, WANG Y J, CHEN Y H, et al. Is image super-resolution helpful for other vision tasks?[C]//2016 IEEE Winter Conference on Applications of Computer Vision. Lake Placid: IEEE, 2016: 1-9. |
2 | BAI Y C, ZHANG Y Q, DING M L, et al. SOD-MTGAN: Amall object detection via multi-task generative adversarial network[C]//Proceedings of the European Conference on Computer Vision(ECCV 2018). Munich: Springer, 2018: 210-226. |
3 | BEI Y J, DAMIAN A, HU S J, et al. New techniques for preserving global structure and denoising with low information loss in single-image super-resolution[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Salt Lake City: IEEE, 2018: 987-9877. |
4 | 吴秀秀, 肖珊, 张煜. 基于配准的肺4D-CT图像超分辨率重建[J]. 电子学报, 2015, 43(2): 383-386. |
WU X X, XIAO S, ZHANG Y. Registration based super-resolution reconstruction for lung 4D-CT image[J]. Acta Electronica Sinica, 2015, 43(2): 383-386. (in Chinese) | |
5 | YIN Y, ROBINSON J, ZHANG Y L, et al. Joint super-resolution and alignment of tiny faces[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 12693-12700. |
6 | HUANG Y W, ZHENG F, WANG D Y, et al. Super-resolution and inpainting with degraded and upgraded generative adversarial networks[C]//Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. Yokohama: ACM, 2021: 645-651. |
7 | WANG Z Y, JIANG K, YI P, et al. Ultra-dense GAN for satellite imagery super-resolution[J]. Neurocomputing, 2020, 398: 328-337. |
8 | GAO G W, YU Y, XIE J, et al. Constructing multilayer locality-constrained matrix regression framework for noise robust face super-resolution[J]. Pattern Recognition, 2021, 110: 107539. |
9 | 李云红, 王珍, 张凯兵, 等. 基于学习的图像超分辨重建方法综述[J]. 计算机工程与应用, 2018, 54(15): 13-21. |
LI Y H, WANG Z, ZHANG K B, et al. Survey on example learning-based single image super-resolution technique[J]. Computer Engineering and Applications, 2018, 54(15): 13-21. (in Chinese) | |
10 | 孙旭, 李晓光, 李嘉锋, 等. 基于深度学习的图像超分辨率复原研究进展[J]. 自动化学报, 2017, 43(5): 697-709. |
SUN X, LI X G, LI J F, et al. Review on deep learning based image super-resolution restoration algorithms[J]. Acta Automatica Sinica, 2017, 43(5): 697-709. (in Chinese) | |
11 | 刘颖, 朱丽, 林庆帆, 等. 图像超分辨率技术的回顾与展望[J]. 计算机科学与探索, 2020, 14(2): 181-199. |
LIU Ying, ZHU Li, LIM K P, et al. Review and prospect of image super-resolution technology[J]. Journal of Frontiers of Computer Science and Technology, 2020, 14(2): 181-199. (in Chinese) | |
12 | HARRIS J L. Diffraction and resolving power[J]. Journal of the Optical Society of America, 1964, 54(7): 931-936. |
13 | GOODMAN J W, COX M E. Introduction to fourier optics[J]. Physics Today, 1969, 22(4): 97-101. |
14 | TSAI R Y, HUANG T S. Multiframe image restoration and registration[J]. Advances in Computer Vision and Image Processing, 1984, 1(2): 317-339. |
15 | DUCHON C E. Lanczos filtering in one and two dimensions[J]. Journal of Applied Meteorology, 1979, 18(8): 1016-1022. |
16 | KEYS R. Cubic convolution interpolation for digital image processing[J]. IEEE Transactions on Acoustics, Speech, and Signal Processing, 1981, 29(6): 1153-1160. |
17 | STARK H, OSKOUI P. High-resolution image recovery from image-plane arrays, using convex projections[J]. Journal of the Optical Society of America. A, Optics and Image Science, 1989, 6(11): 1715-1726. |
18 | IRANI M, PELEG S. Improving resolution by image registration[J]. CVGIP: Graphical Models and Image Processing, 1991, 53(3): 231-239. |
19 | SCHULTZ R R, STEVENSON R L. A Bayesian approach to image expansion for improved definition[J]. IEEE Transactions on Image Processing, 1994, 3(3): 233-242. |
20 | 伍政华, 孙明健, 顾宗山, 等. 基于二阶广义方向性全变分的图像超分辨率重建方法[J]. 电子学报, 2017, 45(11): 2625-2632. |
WU Z H, SUN M J, GU Z S, et al. Second-order directional total generalized variation regularization for image super-resolution[J]. Acta Electronica Sinica, 2017, 45(11): 2625-2632. (in Chinese) | |
21 | 王相海, 赵晓阳, 毕晓昀, 等. 小波域多角度轮廓模板变分模型的单幅图像超分辨率重建[J]. 电子学报, 2018, 46(9): 2256-2262. |
WANG X H, ZHAO X Y, BI X Y, et al. Single image super-resolution reconstruction approach based on multi-angle contour templates variational calculus model in wavelet domain[J]. Acta Electronica Sinica, 2018, 46(9): 2256-2262. (in Chinese) | |
22 | FREEMAN W T, PASZTOR E C, CARMICHAEL O T. Learning low-level vision[J]. International Journal of Computer Vision, 2000, 40: 25-47. |
23 | CHANG H, YEUNG D Y, XIONG Y M. Super-resolution through neighbor embedding[C]//Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington: IEEE, 2004: 275-282. |
24 | YANG J C, WRIGHT J, HUANG T, et al. Image super-resolution as sparse representation of raw image patches[C]//2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage: IEEE, 2008: 1-8. |
25 | TIMOFTE R, DE V, GOOL L V. Anchored neighborhood regression for fast example-based super-resolution[C]//2013 IEEE International Conference on Computer Vision. Sydney: IEEE, 2013: 1920-1927. |
26 | 潘宗序, 禹晶, 肖创柏, 等. 基于自适应多字典学习的单幅图像超分辨率算法[J]. 电子学报, 2015, 43(2): 209-216. |
PAN Z X, YU J, XIAO C B, et al. Single image super resolution based on adaptive multi-dictionary learning[J]. Acta Electronica Sinica, 2015, 43(2): 209-216. (in Chinese) | |
27 | PARK S C, PARK M K, KANG M G. Super-resolution image reconstruction: A technical overview[J]. IEEE Signal Processing Magazine, 2003, 20(3): 21-36. |
28 | 江静, 张雪松. 图像超分辨率重建算法综述[J]. 红外技术, 2012, 34(1): 24-30. |
JIANG J, ZHANG X S. A review of super-resolution reconstruction algorithms[J]. Infrared Technology, 2012, 34(1): 24-30. (in Chinese) | |
29 | 苏衡, 周杰, 张志浩. 超分辨率图像重建方法综述[J]. 自动化学报, 2013, 39(8): 1202-1213. |
SU H, ZHOU J, ZHANG Z H. Survey of super-resolution image reconstruction methods[J]. Acta Automatica Sinica, 2013, 39(8): 1202-1213. (in Chinese) | |
30 | YANG W M, ZHANG X C, TIAN Y P, et al. Deep learning for single image super-resolution: A brief review[J]. IEEE Transactions on Multimedia, 2019, 21(12): 3106-3121. |
31 | 史振威, 雷森. 图像超分辨重建算法综述[J]. 数据采集与处理, 2020, 35(1): 1-20. |
SHI Z W, LEI S. Review of image super-resolution reconstruction[J]. Journal of Data Acquisition and Processing, 2020, 35(1): 1-20. (in Chinese) | |
32 | 张德, 林青宇, 郭茂祖. 单幅图像超分辨重建的深度学习方法综述[J]. 计算机工程与应用, 2021, 57(22): 28-41. |
ZHANG D, LIN Q Y, GUO M Z. Review of single image super-resolution based on deep learning[J]. Computer Engineering and Applications, 2021, 57(22): 28-41. (in Chinese) | |
33 | 黄健, 赵元元, 郭苹, 等. 深度学习的单幅图像超分辨率重建方法综述[J]. 计算机工程与应用, 2021, 57(18): 13-23. |
HUANG J, ZHAO Y Y, GUO P, et al. Survey of single image super-resolution based on deep learning[J]. Computer Engineering and Applications, 2021, 57(18): 13-23. (in Chinese) | |
34 | YANG Z Y, SHI P, PAN D. A survey of super-resolution based on deep learning[C]//2020 International Conference on Culture-oriented Science & Technology(ICCST). Beijing: IEEE, 2020: 514-518. |
35 | 李佳星, 赵勇先, 王京华. 基于深度学习的单幅图像超分辨率重建算法综述[J]. 自动化学报, 2021, 47(10): 2341-2363. |
LI J X, ZHAO Y X, WANG J H. A review of single image super-resolution reconstruction algorithms based on deep learning[J]. Acta Automatica Sinica, 2021, 47(10): 2341-2363. (in Chinese) | |
36 | 唐艳秋, 潘泓, 朱亚平, 等. 图像超分辨率重建研究综述[J]. 电子学报, 2020, 48(7): 1407-1420. |
TANG Y Q, PAN H, ZHU Y P, et al. A survey of image super-resolution reconstruction[J]. Acta Electronica Sinica, 2020, 48(7): 1407-1420. (in Chinese) | |
37 | WANG Z H, CHEN J, HOI S C H. Deep learning for image super-resolution: A survey[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(10): 3365-3387. |
38 | ANWAR S, KHAN S, BARNES N. A deep journey into super-resolution[J]. ACM Computing Surveys, 2021, 53(3): 1-34. |
39 | CHEN H G, HE X H, QING L B, et al. Real-world single image super-resolution: A brief review[J]. Information Fusion, 2022, 79: 124-145. |
40 | AGUSTSSON E, TIMOFTE R. NTIRE 2017 challenge on single image super-resolution: Dataset and study[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1122-1131. |
41 | GU S H, LUGMAYR A, DANELLJAN M, et al. DIV8K: DIVerse 8K resolution image dataset[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW). Seoul: IEEE, 2019: 3512-3516. |
42 | CAI J R, ZENG H, YONG H W, et al. Toward real-world single image super-resolution: A new benchmark and a new model[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV). Seoul: IEEE, 2019: 3086-3095. |
43 | WEI P X, XIE Z W, LU H N, et al. Component divide-and-conquer for real-world image super-resolution[C]//Computer Vision-ECCV 2020. Glasgow: Springer, 2020: 101-117. |
44 | ZHANG K, ZUO W M, ZHANG L. Learning a single convolutional super-resolution network for multiple degradations[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 3262-3271. |
45 | GU J J, LU H N, ZUO W M, et al. Blind super-resolution with iterative kernel correction[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach: IEEE, 2019: 1604-1613. |
46 | LUO Z X, HUANG Y, LI S, et al. Unfolding the alternating optimization for blind super resolution[EB/OL]. (2020-11-25)[2022-01-16]. . |
47 | ZHANG K, LIANG J Y, VAN GOOL L, et al. Designing a practical degradation model for deep blind image super-resolution[C]//2021 IEEE/CVF International Conference on Computer Vision(ICCV). Montreal: IEEE, 2021: 4771-4780. |
48 | WANG X T, XIE L B, DONG C, et al. Real-ESRGAN: Training real-world blind super-resolution with pure synthetic data[EB/OL]. (2021-08-17)[2022-01-16]. . |
49 | BELL-KLIGLER S, SHOCHER A, IRANI M. Blind super-resolution kernel estimation using an Internal-GAN[C]//Proceedings of the 33rd International Conference on Neural Information Processing Systems(NeurIPS). Vancouver: Curran Associates, Inc., 2019: 284-293. |
50 | FRITSCHE M, GU S H, TIMOFTE R. Frequency separation for real-world super-resolution[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW). Seoul: IEEE, 2019: 3599-3608. |
51 | JI X Z, TAO G P, CAO Y, et al. Frequency consistent adaptation for real world super resolution[EB/OL]. (2020-12-18)[2022-01-16]. . |
52 | YANG J C, WRIGHT J, HUANG T S, et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 2010, 19(11): 2861-2873. |
53 | TIMOFTE R, DE SMET V, VAN GOOL L. A+: Adjusted anchored neighborhood regression for fast super-resolution[C]//Computer Vision-ACCV 2014. Singapore: Springer, 2015: 111-126. |
54 | SCHULTER S, LEISTNER C, BISCHOF H. Fast and accurate image upscaling with super-resolution forests[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 3791-3799. |
55 | KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1646-1654. |
56 | DONG C, LOY C C, TANG X O. Accelerating the super-resolution convolutional neural network[C]//Computer Vision-ECCV 2016. Amsterdam: Springer, 2016: 391-407. |
57 | LIN T Y, MAIRE M, BELONGIE S, et al. Microsoft COCO: Common objects in context[C]//Computer Vision-ECCV 2014. Zurich: Springer, 2014: 740-755. |
58 | DENG J, DONG W, SOCHER R, et al. ImageNet: A large-scale hierarchical image database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami: IEEE, 2009: 248-255. |
59 | TIMOFTE R, AGUSTSSON E, Van GOOL L, et al. NTIRE 2017 challenge on single image super-resolution: Methods and results[C]//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Honolulu: IEEE, 2017: 1110-1121. |
60 | JI X Z, CAO Y, TAI Y, et al. Real-world super-resolution via kernel estimation and noise injection[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Seattle: IEEE, 2020: 1914-1923. |
61 | IGNATOV A, KOBYSHEV N, TIMOFTE R, et al. DSLR-quality photos on mobile devices with deep convolutional networks[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 3297-3305. |
62 | WANG X T, YU K, DONG C, et al. Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 606-615. |
63 | BEVILACQUA M, ROUMY A, GUILLEMOT C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//Proceedings of the British Machine Vision Conference 2012. Guildford: BMVA Press, 2012: 135.1-135.10 |
64 | ZEYDE R, ELAD M, PROTTER M. On single image scale-up using sparse-representations[C]//Proceedings of the 7th International Conference on Curves and Surfaces. Avignon: Springer, 2012: 711-730. |
65 | MARTIN D, FOWLKES C, TAL D, et al. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics[C]//Proceedings Eighth IEEE International Conference on Computer Vision(ICCV). Vancouver: IEEE, 2001: 416-423. |
66 | HUANG J B, SINGH A, AHUJA N. Single image super-resolution from transformed self-exemplars[C]//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston: IEEE, 2015: 5197-5206. |
67 | FUJIMOTO A, OGAWA T, YAMAMOTO K, et al. Manga109 dataset and creation of metadata[C]//Proceedings of the 1st International Workshop on coMics ANalysis, Processing and Understanding. Cancun: ACM, 2016: (2) 1-5. |
68 | BLAU Y, MECHREZ R, TIMOFTE R, et al. The 2018 PIRM challenge on perceptual image super-resolution[C]//Lecture Notes in Computer Science. Munich: Springer, 2019: 334-355. |
69 | DONG C, LOY C C, HE K M, et al. Learning a deep convolutional network for image super-resolution[C]//Computer Vision-ECCV 2014. Zurich: Springer, 2014: 184-199. |
70 | DONG C, LOY C C, HE K M, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 38(2): 295-307. |
71 | KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1637-1645. |
72 | HU X C, MU H Y, ZHANG X Y, et al. Meta-SR: A magnification-arbitrary network for super-resolution[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach: IEEE, 2019: 1575-1584. |
73 | SHI W Z, CABALLERO J, HUSZÁR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 1874-1883. |
74 | FAN Y C, SHI H H, YU J H, et al. Balanced two-stage residual networks for image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Honolulu: IEEE, 2017: 1157-1164. |
75 | ZHANG Y, LI K, LI K, et al. Residual non-local attention networks for image restoration[C]//Proceedings of the Seventh International Conference on Learning Representations(ICLR 2019). New Orleans: ICLR Press, 2019: 1-18. |
76 | LAI W S, HUANG J B, AHUJA N, et al. Deep laplacian pyramid networks for fast and accurate super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE, 2017: 5835-5843. |
77 | LAI W S, HUANG J B, AHUJA N, et al. Fast and accurate image super-resolution with deep Laplacian pyramid networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(11): 2599-2613. |
78 | CHUDASAMA V, UPLA K. E-ProSRNet: An enhanced progressive single image super-resolution approach[J]. Computer Vision and Image Understanding, 2020, 200: 103038. |
79 | HARIS M, SHAKHNAROVICH G, UKITA N. Deep back-projection networks for super-resolution[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1664-1673. |
80 | HARIS M, SHAKHNAROVICH G, UKITA N. Deep back-Projection Networks for single image super-resolution[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43(12): 4323-4337. |
81 | HAN W, CHANG S Y, LIU D, et al. Image super-resolution via dual-state recurrent networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 1654-1663. |
82 | LI Z, YANG J L, LIU Z, et al. Feedback network for image super-resolution[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach: IEEE, 2019: 3862-3871. |
83 | LUGMAYR A, DANELLJAN M, TIMOFTE R, et al. NTIRE 2020 challenge on real-world image super-resolution: Methods and results[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Seattle: IEEE, 2020: 2058-2076. |
84 | STREIJL R C, WINKLER S, HANDS D S. Mean opinion score(MOS) revisited: Methods and applications, limitations and alternatives[J]. Multimedia Systems, 2016, 22(2): 213-227. |
85 | 张凯兵, 朱丹妮, 王珍, 等. 超分辨图像质量评价综述[J]. 计算机工程与应用, 2019, 55(4): 31-40, 47. |
ZHANG K B, ZHU D N, WANG Z, et al. Survey of super-resolution images quality assessment[J]. Computer Engineering and Applications, 2019, 55(4): 31-40, 47. (in Chinese) | |
86 | 高敏娟, 党宏社, 魏立力, 等. 全参考图像质量评价回顾与展望[J]. 电子学报, 2021, 49(11): 2261-2272. |
GAO M J, DANG H S, WEI L L, et al. Review and prospect of full reference image quality assessment[J]. Acta Electronica Sinica, 2021, 49(11): 2261-2272. (in Chinese) | |
87 | WANG Z, BOVIK A C, SHEIKH H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4): 600-612. |
88 | SHEIKH H R, BOVIK A C, DE VECIANA G. An information fidelity criterion for image quality assessment using natural scene statistics[J]. IEEE Transactions on Image Processing, 2005, 14(12): 2117-2128. |
89 | ZHANG R, ISOLA P, EFROS A A, et al. The unreasonable effectiveness of deep features as a perceptual metric[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 586-595. |
90 | MITTAL A, SOUNDARARAJAN R, BOVIK A C. Making a “completely blind” image quality analyzer[J]. IEEE Signal Processing Letters, 2013, 20(3): 209-212. |
91 | VENKATANATH N, PRANEETH D, BH M C, et al. Blind image quality evaluation using perception based features[C]//2015 Twenty First National Conference on Communications(NCC). Mumbai: IEEE, 2015: 1-6. |
92 | MA C, YANG C Y, YANG X K, et al. Learning a no-reference quality metric for single-image super-resolution[J]. Computer Vision and Image Understanding, 2017, 158: 1-16. |
93 | LEDIG C, THEIS L, HUSZÁR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu: IEEE, 2017: 105-114. |
94 | CHEN H T, WANG Y H, GUO T Y, et al. Pre-trained image processing transformer[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Nashville: IEEE, 2021: 12294-12305. |
95 | HUBEL D H, WIESEL T N. Receptive fields, binocular interaction and functional architecture in the cat's visual cortex[J]. The Journal of Physiology, 1962, 160(1): 106-154. |
96 | LI Z W, LIU F, YANG W J, et al. A survey of convolutional neural networks: Analysis, applications, and prospects[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021: 1-21. |
97 | HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas: IEEE, 2016: 770-778. |
98 | MAO X, SHEN C, YANG Y. Image restoration using convolutional auto-encoders with symmetric skip connections[C]//Proceedings of the Advances in Neural Information Processing Systems 29(NIPS 2016)[C]. Barcelona: Curran Associates, Inc., 2016. |
99 | LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Honolulu: IEEE, 2017: 1132-1140. |
100 | LI J, FANG F, MEI K, et al. Multi-scale residual network for image super-resolution[C]//Proceedings of the European Conference on Computer Vision. Munich: Springer, 2018: 527-542. |
101 | LAN R S, SUN L, LIU Z B, et al. Cascading and enhanced residual networks for accurate single-image super-resolution[J]. IEEE Transactions on Cybernetics, 2021, 51(1): 115-125. |
102 | 南方哲, 钱育蓉, 行艳妮, 等. 基于深度学习的单图像超分辨率重建研究综述[J]. 计算机应用研究, 2020, 37(2): 321-326. |
NAN F Z, QIAN Y R, XING Y N, et al. Survey of single image super resolution based on deep learning[J]. Application Research of Computers, 2020, 37(2): 321-326. (in Chinese) | |
103 | 杨祎玥, 伏潜, 万定生. 基于深度循环神经网络的时间序列预测模型[J]. 计算机技术与发展, 2017, 27(3): 35-38, 43. |
YANG Y Y, FU Q, WAN D S. A prediction model for time series based on deep recurrent neural network[J]. Computer Technology and Development, 2017, 27(3): 35-38, 43. (in Chinese) | |
104 | TAI Y, YANG J, LIU X M. Image super-resolution via deep recursive residual network[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE, 2017: 2790-2798. |
105 | HUANG G, LIU Z, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Honolulu: IEEE, 2017: 2261-2269. |
106 | TONG T, LI G, LIU X J, et al. Image super-resolution using dense skip connections[C]//2017 IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 4809-4817. |
107 | TAI Y, YANG J, LIU X M, et al. MemNet: A persistent memory network for image restoration[C]//2017 IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 4549-4557. |
108 | SHAMSOLMOALI P, ZAREAPOOR M, ZHANG J H, et al. Image super resolution by dilated dense progressive network[J]. Image and Vision Computing, 2019, 88: 9-18. |
109 | PAN Z, LI B, XI T, et al. Real image super resolution via heterogeneous model ensemble using GP-NAS[C]//Proceedings of the European Conference on Computer Vision(ECCV). Glasgow: Springer, 2020: 423-436. |
110 | JIANG K, WANG Z Y, YI P, et al. Hierarchical dense recursive network for image super-resolution[J]. Pattern Recognition, 2020, 107: 107475. |
111 | CHAUDHARI S, MITHAL V, POLATKAN G, et al. An attentive survey of attention models[J]. ACM Transactions on Intelligent Systems and Technology, 2021, 12(5): 53(1-32. |
112 | HU J, SHEN L, ALBANIE S, et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020,42(8): 2011-2023. |
113 | ZHANG Y, LI K, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]//Proceedings of the European Conference on Computer Vision(ECCV). Munich: Springer, 2018: 294-310. |
114 | DAI T, CAI J R, ZHANG Y B, et al. Second-order attention network for single image super-resolution[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach: IEEE, 2019: 11057-11066. |
115 | AHN N, KANG B, Fast SOHN K., accurate, and lightweight super-resolution with cascading residual network[C]//Proceedings of the European Conference on Computer Vision(ECCV). Munich: Springer, 2018: 256-272. |
116 | HUI Z, WANG X M, GAO X B. Fast and accurate single image super-resolution via information distillation network[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 2018: 723-731. |
117 | HUI Z, GAO X, YANG Y, et al. Lightweight image super-resolution with information multi-distillation network[C]//Proceedings of the 27th ACM International Conference on Multimedia. Nice: ACM, 2019: 2024-2032. |
118 | LIU J, TANG J, WU G. Residual feature distillation network for lightweight image super-resolution[C]//Proceedings of the European Conference on Computer Vision(ECCV). Glasgow: Springer, 2020: 41-55. |
119 | CHU X X, ZHANG B, MA H L, et al. Fast, accurate and lightweight super-resolution with neural architecture search[C]//2020 25th International Conference on Pattern Recognition(ICPR). Milan: IEEE, 2021: 59-64. |
120 | LI W B, ZHOU K, QI L, et al. LAPAR: Linearly-assembled pixel-adaptive regression network for single image super-resolution and beyond[C]//Proceedings of the 34th International Conference on Neural Information Processing Systems. Vancouver: ACM, 2020: 20343-20355. |
121 | WANG L G, DONG X Y, WANG Y Q, et al. Exploring sparsity in image super-resolution for efficient inference[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Nashville: IEEE, 2021: 4915-4924. |
122 | RADOSAVOVIC I, KOSARAJU R P, GIRSHICK R, et al. Designing network design spaces[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Seattle: IEEE, 2020: 10425-10433. |
123 | ZHANG K, DANELLJAN M, LI Y, et al. AIM 2020 challenge on efficient super-resolution: methods and results[C]//Proceedings of the European Conference on Computer Vision(ECCV). Glasgow: Springer, 2020: 5-40. |
124 | GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]//Proceedings of the 27th International Conference on Neural Information Processing Systems(NIPS 2014). Montreal: MIT Press, 2014: 2672-2680. |
125 | SAJJADI M S M, SCHÖLKOPF B, HIRSCH M. EnhanceNet: single image super-resolution through automated texture synthesis[C]//2017 IEEE International Conference on Computer Vision(ICCV). Venice: IEEE, 2017: 4501-4510. |
126 | WANG X, YU K, WU S, et al. ESRGAN: enhanced super-resolution generative adversarial networks[C]//Proceedings of the European Conference on Computer Vision(ECCV). Munich: Springer, 2018: 63-79. |
127 | WANG X T, YU K, DONG C, et al. Deep network interpolation for continuous imagery effect transition[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Long Beach: IEEE, 2019: 1692-1701. |
128 | SHANG T Z, DAI Q J, ZHU S C, et al. Perceptual extreme super resolution network with receptive field block[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Seattle: IEEE, 2020: 1778-1787. |
129 | ZHANG W L, LIU Y H, DONG C, et al. RankSRGAN: generative adversarial networks with ranker for image super-resolution[C]//2019 IEEE/CVF International Conference on Computer Vision(ICCV). Seoul: IEEE, 2019: 3096-3105. |
130 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach: Curran Associates Inc., 2017: 6000-6010. |
131 | DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16×16 words: Transformers for image recognition at scale[C]//Proceedings of the International Conference on Learning Representations(ICLR 2021). London: ICLR Press, 2021: 1-22. |
132 | CARION N, MASSA F, SYNNAEVE G, et al. End-to-end object detection with transformers[C]//Proceedings of the European Conference on Computer Vision(ECCV 2020). Glasgow: Springer, 2020: 213-229. |
133 | ARNAB A, DEHGHANI M, HEIGOLD G, et al. ViViT: A video vision transformer[C]//2021 IEEE/CVF International Conference on Computer Vision(ICCV). Montreal: IEEE, 2021: 6816-6826. |
134 | BI J R, ZHU Z L, MENG Q L. Transformer in computer vision[C]//2021 IEEE International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology(CEI). Fuzhou: IEEE, 2021: 178-188. |
135 | LIANG J Y, CAO J Z, SUN G L, et al. SwinIR: image restoration using swin transformer[C]//2021 IEEE/CVF International Conference on Computer Vision Workshops(ICCVW). Montreal: IEEE, 2021: 1833-1844. |
136 | LIU Z, LIN Y T, CAO Y, et al. Swin transformer: hierarchical vision transformer using shifted windows[C]//2021 IEEE/CVF International Conference on Computer Vision(ICCV). Montreal: IEEE, 2021: 9992-10002. |
137 | LU Z, LIU H, LI J, et al. Efficient transformer for single image super-resolution[EB/OL]. (2021-10-28)[2022-01-16]. . |
138 | SHOCHER A, COHEN N, IRANI M. Zero-shot super-resolution using deep internal learning[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Salt Lake City: IEEE, 2018: 3118-3126. |
139 | BULAT A, YANG J, TZIMIROPOULOS G. To learn image super-resolution, use a GAN to learn how to do image degradation first[C]//Proceedings of the 15th European Conference on Computer Vision(ECCV). Munich: Springer, 2018: 187-202. |
140 | YUAN Y, LIU S Y, ZHANG J W, et al. Unsupervised image super-resolution using cycle-in-cycle generative adversarial networks[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Salt Lake City: IEEE, 2018: 814-81409. |
141 | ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//2017 IEEE International Conference on Computer Vision. Venice: IEEE, 2017: 2242-2251. |
142 | ZHANG Y B, LIU S Y, DONG C, et al. Multiple cycle-in-cycle generative adversarial networks for unsupervised image super-resolution[J]. IEEE Transactions on Image Processing, 2020, 29: 1101-1112. |
143 | WU S, DONG C, QIAO Y. Blind image restoration based on cycle-consistent network[J/OL]. IEEE Transactions On Multimedia, 2022. DOI: 10.1109/TMM.2021 . |
3139209. | |
144 | TIMOFTE R, ROTHE R, VAN GOOL L. Seven ways to improve example-based single image super resolution[C]//2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas: IEEE, 2016: 1865-1873. |
145 | TIMOFTE R, GU S H, WU J Q, et al. NTIRE 2018 challenge on single image super-resolution: Methods and results[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Salt Lake City: IEEE, 2018: 965-96511. |
146 | CAI J R, GU S H, TIMOFTE R, et al. NTIRE 2019 challenge on real image super-resolution: Methods and results[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Long Beach: IEEE, 2019: 2211-2223. |
147 | LUGMAYR A, DANELLJAN M, TIMOFTE R, et al. AIM 2019 challenge on real-world image super-resolution: Methods and results[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW). Seoul: IEEE, 2019: 3575-3583. |
148 | ZHANG K, GU S H, TIMOFTE R, et al. AIM 2019 challenge on constrained super-resolution: Methods and results[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW). Seoul: IEEE, 2019: 3565-3574. |
149 | GU S H, DANELLJAN M, TIMOFTE R, et al. AIM 2019 challenge on image extreme super-resolution: Methods and results[C]//2019 IEEE/CVF International Conference on Computer Vision Workshop(ICCVW). Seoul: IEEE, 2019: 3556-3564. |
150 | ZHANG K, GU S, TIMOFTE R, et al. NTIRE 2020 challenge on perceptual extreme super-resolution: Methods and results[C]//Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Seattle: IEEE, 2020: 2045-2057. |
151 | WEI P, LU H, TIMOFTE R, et al. AIM 2020 challenge on real image super-resolution: methods and results[C]//Proceedings of the European Conference on Computer Vision(ECCV). Berlin: Springer, 2020: 392-422. |
152 | BHAT G, DANELLJAN M, TIMOFTE R, et al. NTIRE 2021 challenge on burst super-resolution: Methods and results[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Nashville: IEEE, 2021: 613-626. |
153 | IGNATOV A, TIMOFTE R, DENNA M, et al. Real-time quantized image super-resolution on mobile NPUs, mobile AI 2021 challenge: Report[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Nashville: IEEE, 2021: 2525-2534. |
154 | FAN Y C, YU J H, LIU D, et al. Scale-wise convolution for image restoration[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(7): 10770-10777. |
155 | YOO J, CHEN Q. SinIR: Efficient general image manipulation with single image reconstruction[C]//Proceedings of the 38th International Conference on Machine Learning(ICML). Champaign: PMLR, 2021: 12040-12050. |
156 | KONG X T, ZHAO H Y, QIAO Y, et al. ClassSR: A general framework to accelerate super-resolution networks by data characteristic[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). Nashville: IEEE, 2021: 12011-12020. |
157 | WANG L G, WANG Y Q, LIN Z P, et al. Learning a single network for scale-arbitrary super-resolution[C]//2021 IEEE/CVF International Conference on Computer Vision(ICCV). Montreal: IEEE, 2021: 4781-4790. |
158 | AYAZOGLU M. Extremely lightweight quantization robust real-time single-image super resolution for mobile devices[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops(CVPRW). Nashville: IEEE, 2021: 2472-2479. |
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