Super resolution Survey

List of reviewing papers

• SRCNN_2014_ECCV_Learning a Deep Convolutional Network for Image Super-Resolution
• SRCNN_2016_PAMI_Image Super-Resolution Using Deep Convolutional Networks
  • review
• CSCN_2015_ICCV_Deep Networks for Image Super-Resolution with Sparse Prior
  • cvf: https://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Wang_Deep_Networks_for_ICCV_2015_paper.pdf
  • page: http://www.ifp.illinois.edu/~dingliu2/iccv15/
  • review
• CSCN_2016_ACCV_Learning a Mixture of Deep Networks for Single Image Super-Resolution
  • arxiv: https://arxiv.org/pdf/1701.00823.pdf
  • review
• CSCN_2016_PAMI_Robust Single Image Super-Resolution via Deep Networks with Sparse Prior
  • review
• DRCN_2016_CVPR_Deeply-Recursive Convolutional Network for Image Super-Resolution
  • arxiv: https://arxiv.org/abs/1511.04491
  • review
• EnhanceNet_2016_ArXiv_Single Image Super_resolution through Automated Texture Synthesis
  • arxiv: https://arxiv.org/abs/1612.07919
  • github(tensorflow): https://github.com/msmsajjadi/EnhanceNet-Code
  • review
• FSRCNN_2016_ECCV_Accelerating the Super-Resolution Convolutional Neural Network
  • review
• VDSR_2016_CVPR_Accurate Image Super_Resolution Using Very Deep Convolutional Networks
  • review
• VESPCN_2016_ArXiv_Real-Time Video Super-Resolution with Spatio-Temporal Networks and Motion Compensation
  • arxiv: https://arxiv.org/abs/1611.05250
  • review
• AffGAN_2017_ICLR_Amortised Map Inference for Image Super_Resolution
• DEGREE_2017_TIP_Deep Edge Guided Recurrent Residual Learning for Image Super-Resolution
  • arxiv: https://arxiv.org/abs/1604.08671
  • review
• DRNN_2017_CVPR_Image Super-Resolution via Deep Recursive Residual Network
  • review
• EDSR_2017_CVPRW_Enhanced Deep Residual Networks for Single Image Super-Resolution
  • arxiv: https://arxiv.org/abs/1707.02921
  • github(Pytorch): https://github.com/thstkdgus35/EDSR-PyTorch
  • github(Torch): https://github.com/LimBee/NTIRE2017
  • review
• GUN_2017_ArXiv_Gradual Upsampling Network for single image super-resolution
  • review
• LapSRN_2017_CVPR_Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
  • review
• SRGAN_2017_CVPR_Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
  • arxiv: https://arxiv.org/abs/1609.04802
  • review
• CincGan_2018_CVPR_Unsupervised Image Super-Resolution using Cycle-in-Cycle Generative Adversarial Networks
  • review
• EMBSR_2018_CVPR_Efficient Module Based Single Image Super Resolution for Multiple Problems
  • cvf
  • review
• ProSR_2018_CVPR_A Fully Progressive Approach to Single-Image Super-Resolution
  • review
• DRRN_2018_CVPR_Image Super-Resolution via Deep Recursive Residual Network
• LRFNets_2018_CVPR_Large Receptive Field Networks for High-Scale Image Super-Resolution
  • review
• D-DBPN_2018_CVPR_Deep Back-Projection Networks For Super-Resolution
  • review
• RDN_2018_CVPR_Residual Dense Network for Image Super-Resolution
  • review
• SFT-GAN_2018_CVPR_Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
  • website: http://mmlab.ie.cuhk.edu.hk/projects/SFTGAN/
  • github: https://github.com/xinntao/CVPR18-SFTGAN
  • review
• 2018_CVPR_Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation
  • review
• 2018_CVPR_SRMD_Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
  • cvf
  • github: https://github.com/cszn/SRMD
  • review
• Deep Photo Enhancer: Unpaired Learning for Image Enhancement from Photographs with GANs
  • cvf
  • github: https://github.com/nothinglo/Deep-Photo-Enhancer
  • review
• RCAN_2018_ECCV_Image_Super-Resolution Using Very Deep Residual Channel Attention Networkds
  • arxiv: https://arxiv.org/abs/1807.02758
  • cvf
  • supplementary
  • github(Pytorch): https://github.com/yulunzhang/RCAN
  • review