srcnn论文格式

最近要学做一些关于低剂量重建得工作，花了两天时间专门看了一下SRCNN[1]的论文，以下是自己学习的一些总结。     首先，SRCNN的网络结构如下：     SRCNN是一个用于图像图像超分辨的CNN网络，也是用CNN做超分辨的开山之作。在原文中，作者设计了一个三层的全卷积神经网络。SRCNN算法流程如下：     1.首先对输入的低分辨率的图像使用双三次插值放大至目标尺寸。     2.将低分辨率图像输入三层卷积神经网络。     在SRCNN的训练过程中，作者采用平均均方差（MSE：mean squared error）来作为损失函数，并且为了在训练过程中避免边界效应，并未对图像进行填充。在训练过程中前两层的学习率设为，最后一层学习率为。在训练过程中，图片采用的是YCbCr格式的图像，并只对Y通道的图像进行超分辨处理，因此输入的通道数c=1。通过对比不同的数据集以及网络结构，作者得到如下结论。     1.大量的数据集会对训练的结果有一定提升作用，但提升作用不像其他视觉任务那样明显。     2.增加网络的宽度（filter数量）会显著的提升训练结果。         3.增加filter的size可以收集更丰富的结构信息，从而得到更好的结果。     4.增加网络层数不会对结果有明显的提升作用，反而会增加训练的难度。     5.在RGB图像上实现最好的效果，与单通道（Y-only）相比提升并不明显。加入CrCb反而不利于训练。

Meta-SR: A Magnification-Arbitrary Network for Super-Resolution Xuecai Hu, Haoyuan Mu, Xiangyu Zhang, Zilei Wang, Tieniu Tan, Jian Sun [ pdf ]

Blind Super-Resolution With Iterative Kernel Correction Jinjin Gu, Hannan Lu, Wangmeng Zuo, Chao Dong [ pdf ]

Camera Lens Super-Resolution Chang Chen, Zhiwei Xiong, Xinmei Tian, Zheng-Jun Zha, Feng Wu [ pdf ], [ supp ]

Deep Plug-And-Play Super-Resolution for Arbitrary Blur Kernels Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]

Towards Real Scene Super-Resolution With Raw Images Xiangyu Xu, Yongrui Ma, Wenxiu Sun [ pdf ]

ODE-Inspired Network Design for Single Image Super-Resolution Xiangyu He, Zitao Mo, Peisong Wang, Yang Liu, Mingyuan Yang, Jian Cheng [ pdf ], [ supp ]

Feedback Network for Image Super-Resolution Zhen Li, Jinglei Yang, Zheng Liu, Xiaomin Yang, Gwanggil Jeon, Wei Wu [ pdf ], [ supp ]

Recurrent Back-Projection Network for Video Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ], [ supp ]

Image Super-Resolution by Neural Texture Transfer Zhifei Zhang, Zhaowen Wang, Zhe Lin, Hairong Qi [ pdf ]

Natural and Realistic Single Image Super-Resolution With Explicit Natural Manifold Discrimination Jae Woong Soh, Gu Yong Park, Junho Jo, Nam Ik Cho [ pdf ], [ supp ]

3D Appearance Super-Resolution With Deep Learning Yawei Li, Vagia Tsiminaki, Radu Timofte, Marc Pollefeys, Luc Van Gool [ pdf ], [ supp ]

Fast Spatio-Temporal Residual Network for Video Super-Resolution Sheng Li, Fengxiang He, Bo Du, Lefei Zhang, Yonghao Xu, Dacheng Tao [ pdf ], [ supp ]

Residual Networks for Light Field Image Super-Resolution Shuo Zhang, Youfang Lin, Hao Sheng [ pdf ]

Second-Order Attention Network for Single Image Super-Resolution Tao Dai, Jianrui Cai, Yongbing Zhang, Shu-Tao Xia, Lei Zhang [ pdf ], [ pdf ]

Hyperspectral Image Super-Resolution With Optimized RGB Guidance Ying Fu, Tao Zhang, Yinqiang Zheng, Debing Zhang, Hua Huang [ pdf ]

Learning Parallax Attention for Stereo Image Super-Resolution Longguang Wang, Yingqian Wang, Zhengfa Liang, Zaiping Lin, Jungang Yang, Wei An, Yulan Guo [ pdf ], [ supp ]

Face Super-resolution Guided by Facial Component Heatmaps Xin Yu, Basura Fernando, Bernard Ghanem, Fatih Porikli, Richard Hartley [ pdf ]

Image Super-Resolution Using Very Deep Residual Channel Attention Networks Yulun Zhang, Kunpeng Li, Kai Li, Lichen Wang, Bineng Zhong, Yun Fu [ pdf ]

Super-Resolution and Sparse View CT Reconstruction Guangming Zang, Mohamed Aly, Ramzi Idoughi, Peter Wonka, Wolfgang Heidrich [ pdf ]

Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network Namhyuk Ahn, Byungkon Kang, Kyung-Ah Sohn [ pdf ]

SRFeat: Single Image Super-Resolution with Feature Discrimination Seong-Jin Park, Hyeongseok Son, Sunghyun Cho, Ki-Sang Hong, Seungyong Lee [ pdf ]

To learn image super-resolution, use a GAN to learn how to do image degradation first Adrian Bulat, Jing Yang, Georgios Tzimiropoulos [ pdf ]

Multi-scale Residual Network for Image Super-Resolution Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang [ pdf ]

Super-FAN: Integrated Facial Landmark Localization and Super-Resolution of Real-World Low Resolution Faces in Arbitrary Poses With GANs Adrian Bulat, Georgios Tzimiropoulos [ pdf ] [ Supp ]

Fight Ill-Posedness With Ill-Posedness: Single-Shot Variational Depth Super-Resolution From Shading Bjoern Haefner, Yvain Quéau, Thomas Möllenhoff, Daniel Cremers [ pdf ] [ Supp ]

Recovering Realistic Texture in Image Super-Resolution by Deep Spatial Feature Transform Xintao Wang, Ke Yu, Chao Dong, Chen Change Loy [ pdf ]

Fast and Accurate Single Image Super-Resolution via Information Distillation Network Zheng Hui, Xiumei Wang, Xinbo Gao [ pdf ]

Image Super-Resolution via Dual-State Recurrent Networks Wei Han, Shiyu Chang, Ding Liu, Mo Yu, Michael Witbrock, Thomas S. Huang [ pdf ]

Deep Back-Projection Networks for Super-Resolution Muhammad Haris, Gregory Shakhnarovich, Norimichi Ukita [ pdf ] [ Supp ]

Residual Dense Network for Image Super-Resolution Yulun Zhang, Yapeng Tian, Yu Kong, Bineng Zhong, Yun Fu [ pdf ]

FSRNet: End-to-End Learning Face Super-Resolution With Facial Priors Yu Chen, Ying Tai, Xiaoming Liu, Chunhua Shen, Jian Yang [ pdf ]

Unsupervised Sparse Dirichlet-Net for Hyperspectral Image Super-Resolution Ying Qu, Hairong Qi, Chiman Kwan [ pdf ]

“Zero-Shot” Super-Resolution Using Deep Internal Learning Assaf Shocher, Nadav Cohen, Michal Irani [ pdf ]

Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation Younghyun Jo, Seoung Wug Oh, Jaeyeon Kang, Seon Joo Kim [ pdf ] [ Supp ]

Learning a Single Convolutional Super-Resolution Network for Multiple Degradations Kai Zhang, Wangmeng Zuo, Lei Zhang [ pdf ]

Feature Super-Resolution: Make Machine See More Clearly Weimin Tan, Bo Yan, Bahetiyaer Bare [ pdf ]

Frame-Recurrent Video Super-Resolution Mehdi S. M. Sajjadi, Raviteja Vemulapalli, Matthew Brown [ pdf ]

Temporal Shape Super-Resolution by Intra-Frame Motion Encoding Using High-Fps Structured Light Yuki Shiba, Satoshi Ono, Ryo Furukawa, Shinsaku Hiura, Hiroshi Kawasaki [ pdf ] [ Supp ] [ video ]

Robust Video Super-Resolution With Learned Temporal Dynamics Ding Liu, Zhaowen Wang, Yuchen Fan, Xianming Liu, Zhangyang Wang, Shiyu Chang, Thomas Huang [ pdf ]

Detail-Revealing Deep Video Super-Resolution Xin Tao, Hongyun Gao, Renjie Liao, Jue Wang, Jiaya Jia [ pdf ] [ video ]

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis Mehdi S. M. Sajjadi, Bernhard Scholkopf, Michael Hirsch [ pdf ] [ Supp ][ video ]

Joint Estimation of Camera Pose, Depth, Deblurring, and Super-Resolution From a Blurred Image Sequence Haesol Park, Kyoung Mu Lee [ pdf ] [ Supp ]

Image Super-Resolution Using Dense Skip Connections Tong Tong, Gen Li, Xiejie Liu, Qinquan Gao [ pdf ]

Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution Wei-Sheng Lai, Jia-Bin Huang, Narendra Ahuja, Ming-Hsuan Yang [ pdf ]

Image Super-Resolution via Deep Recursive Residual Network Ying Tai, Jian Yang, Xiaoming Liu [ pdf ]

Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network Christian Ledig, Lucas Theis, Ferenc Huszar, Jose Caballero, Andrew Cunningham, Alejandro Acosta, Andrew Aitken, Alykhan Tejani, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ] [ video ]

Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation Jose Caballero, Christian Ledig, Andrew Aitken, Alejandro Acosta, Johannes Totz, Zehan Wang, Wenzhe Shi [ pdf ] [ poster ]

Hyperspectral Image Super-Resolution via Non-Local Sparse Tensor Factorization Renwei Dian, Leyuan Fang, Shutao Li [ pdf ] [ poster ]

Simultaneous Super-Resolution and Cross-Modality Synthesis of 3D Medical Images Using Weakly-Supervised Joint Convolutional Sparse Coding Yawen Huang, Ling Shao, Alejandro F. Frangi [ pdf ] [ poster ]

Reference Guided Deep Super-Resolution via Manifold Localized External Compensation Wenhan Yang, Sifeng Xia, Jiaying Liu, and Zongming Guo Accepted by IEEE Trans. on Circuit System for Video Technology (TCSVT), June 2018. [ project ]

Joint-Feature Guided Depth Map Super-Resolution With Face Priors Shuai Yang, Jiaying Liu, Yuming Fang, and Zongming Guo IEEE Trans. on Cybernetics (TCYB), , , , Jan. 2018. [ project ]

Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution Wenhan Yang, Jiashi Feng, Jianchao Yang, Fang Zhao, Jiaying Liu, Zongming Guo and Shuicheng Yan IEEE Trans. on Image Processing (TIP), , , , Dec. 2017. [ project ]

Retrieval Compensated Group Structured Sparsity for Image Super-Resolution Jiaying Liu, Wenhan Yang, Xinfeng Zhang, and Zongming Guo IEEE Trans. on Multimedia (TMM), , , , Feb. 2017. [ project ]

ECCV2016 Accelerating the Super-Resolution Convolutional Neural Network Chao Dong, Chen Change Loy, Xiaoou Tang [ project ]

Perceptual Losses for Real-Time Style Transfer and Super-Resolution Justin Johnson, Alexandre Alahi, Li Fei-Fei [ pdf ]

End-to-End Image Super-Resolution via Deep and Shallow Convolutional Networks Yifan Wang, Lijun Wang, Hongyu Wang, Peihua Li [ pdf ]

Deeply-Recursive Convolutional Network for Image Super-Resolution Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]

Accurate Image Super-Resolution Using Very Deep Convolutional Networks Jiwon Kim, Jung Kwon Lee, Kyoung Mu Lee [ pdf ]

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network Wenzhe Shi, Jose Caballero, Ferenc Huszar, Johannes Totz, Andrew P. Aitken, Rob Bishop, Daniel Rueckert, Zehan Wang [ pdf ]

Image Super-Resolution Using Deep Convolutional Networks Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) SRCNN [ project ] [ pdf ] [ supplementary material ]

Neighborhood Regression for Edge-Preserving Image Super-Resolution Yanghao Li, Jiaying Liu, Wenhan Yang and Zongming Guo IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brisbane, Australia, Apr. 2015. [ project ]

Learning a Deep Convolutional Network for Image Super-Resolution Chao Dong, Chen Change Loy, Kaiming He, Xiaoou Tang ECCV 2014 SRCNN [ project ] [ pdf ]

Super-Resolution From a Single Image Daniel Glasner, Shai Bagon, Michal Irani ICCV 2009 [ project ] [ pdf ]

Video Super-Resolution Based on Spatial-Temporal Recurrent Neural Networks Wenhan Yang, Jiashi Feng, Guosen Xie, Jiaying Liu, Zongming Guo and Shuicheng Yan Computer Vision and Image Understanding (CVIU), , , March. 2018. [ project ]

Video Super-Resolution With Convolutional Neural Networks Armin Kappeler ; Seunghwan Yoo ; Qiqin Dai ; Aggelos K. Katsaggelos IEEE Transactions on Computational Imaging [ pdf ]

High Quality Remote Sensing Image Super-Resolution Using Deep Memory Connected Network Wenjia Xu; Guangluan XU; Yang Wang; Xian Sun; Daoyu Lin; Yirong WU IGARSS 2018 - 2018 IEEE International Geoscience and Remote SensingSymposium [ pdf ]