超分辨率重建部分算法总结1

<link rel="stylesheet" href="https://csdnimg.cn/release/phoenix/template/css/ck_htmledit_views-3d4dc5c1de.css"> <div class="htmledit_views" id="content_views"> <p>超分辨率资源的精确列表和单图像超分辨率算法的基准。<br>

请参阅我实现的超分辨率算法：

SRGANVDSRCSCN

TODO

Build a benckmark like SelfExSR_Code

State-of-the-art algorithms：

Classical Sparse Coding Method 经典稀疏编码

ScSR [Web]Image super-resolution as sparse representation of raw image patches (CVPR2008), Jianchao Yang et al.

        基于原始图像块稀疏表示的图像超分辨率

Image super-resolution via sparse representation (TIP2010), Jianchao Yang et al.

       基于稀疏表示的图像超分辨率

Coupled dictionary training for image super-resolution (TIP2011), Jianchao Yang et al.

      基于耦合字典训练的图像超分辨率重建

Anchored Neighborhood Regression Method 锚定邻域回归方法

ANR [Web]

         Anchored Neighborhood Regression for Fast Example-Based Super-Resolution (ICCV2013), Radu Timofte et al.

        基于邻域快速回归的快速实例超分辨率

A+  [Web]

        A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution (ACCV2014), Radu Timofte et al.

IA  [Web]

        Seven ways to improve example-based single image super resolution (CVPR2016), Radu Timofte et al.

Self-Exemplars

SelfExSR 

Single Image Super-Resolution from Transformed Self-Exemplars (CVPR2015), Jia-Bin Huang et al.

Bayes

NBSRF 

Naive Bayes Super-Resolution Forest (ICCV2015), Jordi Salvador et al.

朴素贝叶斯超分辨率森林

Deep Learning Method

SRCNN 

Image Super-Resolution Using Deep Convolutional Networks (ECCV2014), Chao Dong et al.Image Super-Resolution Using Deep Convolutional Networks (TPAMI2015), Chao Dong et al.

CSCN 

Deep Networks for Image Super-Resolution with Sparse Prior (ICCV2015), Zhaowen Wang et al.Robust Single Image Super-Resolution via Deep Networks with Sparse Prior (TIP2016), Ding Liu et al.

VDSR 

Accurate Image Super-Resolution Using Very Deep Convolutional Networks (CVPR2016), Jiwon Kim et al.

DRCN

Deeply-Recursive Convolutional Network for Image Super-Resolution (CVPR2016), Jiwon Kim et al.基于深度递归卷积网络的图像超分辨率重建

ESPCN

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network (CVPR2016), Wenzhe Shi et al.基于高效亚像素卷积神经网络的实时单图像和视频超分辨率Is the deconvolution layer the same as a convolutional layer? Checkerboard artifact free sub-pixel convolution 

FSRCNN

Acclerating the Super-Resolution Convolutional Neural Network (ECCV2016), Dong Chao et al.

LapSRN

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution (CVPR 2017), Wei-Sheng Lai et al.基于深拉普拉斯金字塔网络的快速和准确的超分辨率

EDSR 

Enhanced Deep Residual Networks for Single Image Super-Resolution (Winner of NTIRE2017 Super-Resolution Challenge), Bee Lim et al.

Perceptual Loss and GAN（损失函数上改进）

Perceptual Loss 

Perceptual Losses for Real-Time Style Transfer and Super-Resolution (ECCV2016), Justin Johnson et al.基于感知损失的实时风格转移和超分辨率

SRGAN

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network (CVPR2017), Christian Ledig et al.基于生成对抗网络的逼真图片的单一图像超分辨率

AffGAN 

AMORTISED MAP INFERENCE FOR IMAGE SUPER-RESOLUTION (ICLR2017), Casper Kaae Sønderby et al.

EnhanceNet 

EnhanceNet: Single Image Super-Resolution through Automated Texture Synthesis, Mehdi S. M. Sajjadi et al.

Video SR

VESPCN 

Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation (CVPR2017), Jose Caballero et al.

来自 https://github.com/huangzehao/Super-Resolution.Benckmark