Awesome-SuperResolution

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Awesome-SuperResolution

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awesome super resoluiton paper collections w/o code including paper citation

Awesome-SuperResolution 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# Awesome-SuperResolution

|Year|Publication|Title|Code|Citation|
|:---:|:---:|:---:|:---:|:---:|
|2019|IEEE Access|End-to-end image super-resolution via deep and shallow convolutional networks|-|42
|2019|arxiv|[Deep learning for image super-resolution: A survey](https://arxiv.org/pdf/1902.06068.pdf)|-|4
|2019|arxiv|[Toward Real-World Single Image Super-Resolution:A New Benchmark and A New Model](https://arxiv.org/pdf/1904.00523.pdf)|-|3
|2019|CVPR|[Ntire 2019 challenge on real image denoising: Methods and results](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Abdelhamed_NTIRE_2019_Challenge_on_Real_Image_Denoising_Methods_and_Results_CVPRW_2019_paper.pdf)|-|4
|2019|CVPR|[Deep Plug-and-Play Super-Resolution for Arbitrary Blur Kernels](http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Deep_Plug-And-Play_Super-Resolution_for_Arbitrary_Blur_Kernels_CVPR_2019_paper.pdf)|[Pytorch](https://github.com/cszn/DPSR)|1
|2019|CVPR|[Second-order Attention Network for Single Image Super-resolution](http://openaccess.thecvf.com/content_CVPR_2019/papers/Dai_Second-Order_Attention_Network_for_Single_Image_Super-Resolution_CVPR_2019_paper.pdf)|-|0
|2019|CVPRW|[Encoder-Decoder Residual Network for Real Super-Resolution](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Cheng_Encoder-Decoder_Residual_Network_for_Real_Super-Resolution_CVPRW_2019_paper.pdf)|-|1
|2019|CVPRW|[EDVR: Video Restoration with Enhanced Deformable Convolutional Networks](http://openaccess.thecvf.com/content_CVPRW_2019/papers/NTIRE/Wang_EDVR_Video_Restoration_With_Enhanced_Deformable_Convolutional_Networks_CVPRW_2019_paper.pdf)|[Pytorch](https://github.com/xinntao/EDVR)|2
|2018|ECCV|[Fast, accurate, and lightweight super-resolution with cascading residual network](http://openaccess.thecvf.com/content_ECCV_2018/papers/Namhyuk_Ahn_Fast_Accurate_and_ECCV_2018_paper.pdf)|-|30|
|2018|ECCV|[Image super-resolution using very deep residual channel attention networks](http://openaccess.thecvf.com/content_ECCV_2018/papers/Yulun_Zhang_Image_Super-Resolution_Using_ECCV_2018_paper.pdf)|-|103|
|2018|ECCV|[To learn image super-resolution, use a GAN to learn how to do image degradation first](http://openaccess.thecvf.com/content_ECCV_2018/papers/Adrian_Bulat_To_learn_image_ECCV_2018_paper.pdf)|-|17
|2018|ECCV|[Face super-resolution guided by facial component heatmaps](http://openaccess.thecvf.com/content_ECCV_2018/papers/Xin_Yu_Face_Super-resolution_Guided_ECCV_2018_paper.pdf)|-|7|
|2018|ECCV|[Multi-scale residual network for image super-resolution](http://openaccess.thecvf.com/content_ECCV_2018/papers/Juncheng_Li_Multi-scale_Residual_Network_ECCV_2018_paper.pdf)|-|11
|2018|ECCV|[CrossNet: An End-to-end Reference-based Super Resolution Network using Cross-scale Warping](http://openaccess.thecvf.com/content_ECCV_2018/papers/Haitian_Zheng_CrossNet_An_End-to-end_ECCV_2018_paper.pdf)|-|5
|2018|ECCV|[Srfeat: Single image super-resolution with feature discrimination](http://openaccess.thecvf.com/content_ECCV_2018/papers/Seong-Jin_Park_SRFeat_Single_Image_ECCV_2018_paper.pdf)|-|9|
|2018|ECCVW|[The unreasonable effectiveness of texture transfer for single image super-resolution](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Gondal_The_Unreasonable_Effectiveness_of_Texture_Transfer_for_Single_Image_Super-resolution_ECCVW_2018_paper.pdf)|-|6
|2018|ECCVW|[The 2018 PIRM challenge on perceptual image super-resolution](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Blau_2018_PIRM_Challenge_on_Perceptual_Image_Super-resolution_ECCVW_2018_paper.pdf)|-|38
|2018|ECCVW|[PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Ignatov_PIRM_Challenge_on_Perceptual_Image_Enhancement_on_Smartphones_Report_ECCVW_2018_paper.pdf)|-|16|
|2018|ECCVW|[Esrgan: Enhanced super-resolution generative adversarial networks](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Wang_ESRGAN_Enhanced_Super-Resolution_Generative_Adversarial_Networks_ECCVW_2018_paper.pdf)|[PyTorch](https://github.com/xinntao/ESRGAN)|63
|2018|ECCV|[Generative adversarial network-based image super-resolution using perceptual content losses](http://openaccess.thecvf.com/content_ECCVW_2018/papers/11133/Cheon_Generative_Adversarial_Network-based_Image_Super-Resolution_using_Perceptual_Content_Losses_ECCVW_2018_paper.pdf)|-|3
|2018|CVPR|[Fast and accurate single image super-resolution via information distillation network](http://openaccess.thecvf.com/content_cvpr_2018/papers/Hui_Fast_and_Accurate_CVPR_2018_paper.pdf)|[Caffe](https://github.com/Zheng222/IDN-Caffe)|43|
|2018|CVPR|[Image super-resolution via dual-state recurrent networks](http://openaccess.thecvf.com/content_cvpr_2018/papers/Han_Image_Super-Resolution_via_CVPR_2018_paper.pdf)|[Tensorflow](https://github.com/weihan3/dsrn)|24|
|2018|CVPR|[Deep back-projection networks for super-resolution](http://openaccess.thecvf.com/content_cvpr_2018/papers/Haris_Deep_Back-Projection_Networks_CVPR_2018_paper.pdf)|-|101|
|2018|CVPR|[A fully progressive approach to single-image super-resolution](https://arxiv.org/pdf/1710.01992.pdf)|-|28|
|2018|CVPR|[FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors](http://openaccess.thecvf.com/content_cvpr_2018/papers/Chen_FSRNet_End-to-End_Learning_CVPR_2018_paper.pdf)|[Torch](https://github.com/tyshiwo/FSRNet)|36
|2018|CVPR|[Residual dense network for image super-resolution](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Residual_Dense_Network_CVPR_2018_paper.pdf)|[Torch](https://github.com/yulunzhang/RDN)|153
|2018|CVPR|[Recovering realistic texture in image super-resolution by deep spatial feature transform](http://openaccess.thecvf.com/content_cvpr_2018/papers/Wang_Recovering_Realistic_Texture_CVPR_2018_paper.pdf)|-|59|
|2018|CVPR|[Learning a single convolutional super-resolution network for multiple degradations](http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Learning_a_Single_CVPR_2018_paper.pdf)|-|59
|2018|CVPR|[Deep video super-resolution network using dynamic upsampling filters without explicit motion compensation](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jo_Deep_Video_Super-Resolution_CVPR_2018_paper.pdf)|[Tensorflow](https://github.com/yhjo09/VSR-DUF)|19|
|2018|CVPR|[Frame-recurrent video super-resolution](http://openaccess.thecvf.com/content_cvpr_2018/papers/Sajjadi_Frame-Recurrent_Video_Super-Resolution_CVPR_2018_paper.pdf)|-|29
|2018|CVPRW|[Deep residual network with enhanced upscaling module for super-resolution](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Kim_Deep_Residual_Network_CVPR_2018_paper.pdf)|-|9|
|2018|CVPRW|[Image super-resolution via progressive cascading residual network](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Ahn_Image_Super-Resolution_via_CVPR_2018_paper.pdf)|-|2|
|2018|CVPRW|[Ntire 2018 challenge on single image super-resolution: Methods and results](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Timofte_NTIRE_2018_Challenge_CVPR_2018_paper.pdf)|-|45|
|2018|CVPRW|[Persistent memory residual network for single image super resolution](Persistent Memory Residual Network for Single Image Super Resolution)|-|3|
|2018|TPAMI|[Fast and accurate image super-resolution with deep laplacian pyramid networks](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w13/Wang_A_Fully_Progressive_CVPR_2018_paper.pdf)|-|55|
|2018|TIP|LFNet: A novel bidirectional recurrent convolutional neural network for light-field image super-resolution|-|11|
|2018|TIP|Learning temporal dynamics for video super-resolution: A deep learning approach|-|15|
|2018|WACV|[CT-SRCNN: cascade trained and trimmed deep convolutional neural networks for image super resolution](https://arxiv.org/pdf/1711.04048.pdf)|-|6
|2018|arxiv|[Single Image Super-Resolution via Cascaded Multi-Scale Cross Network](https://arxiv.org/pdf/1802.08808.pdf)|-|9
|2018|arxiv|[Wide Activation for Efficient and Accurate Image Super-Resolution](https://arxiv.org/pdf/1808.08718.pdf)|[Pytorch](https://github.com/JiahuiYu/wdsr_ntire2018)|13|
|2017|ICLR|[Amortised map inference for image super-resolution](https://arxiv.org/pdf/1610.04490.pdf)|-|178|
|2017|CVPR|[Photo-realistic single image super-resolution using a generative adversarial network](http://openaccess.thecvf.com/content_cvpr_2017/papers/Ledig_Photo-Realistic_Single_Image_CVPR_2017_paper.pdf)|-|1884|
|2017|CVPR|[Enhanced deep residual networks for single image super-resolution](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf)|-|445|
|2017|CVPR|[Image Super Resolution via Deep Recursive Residual Network](http://openaccess.thecvf.com/content_cvpr_2017/papers/Tai_Image_Super-Resolution_via_CVPR_2017_paper.pdf)|[Matlab](https://github.com/tyshiwo/DRRN_CVPR17)|296|
|2017|CVPR|[Deep laplacian pyramid networks for fast and accurate super-resolution](http://openaccess.thecvf.com/content_cvpr_2017/papers/Lai_Deep_Laplacian_Pyramid_CVPR_2017_paper.pdf)|[Matlab](https://github.com/phoenix104104/LapSRN)|378|
|2017|CVPR|[Image Super-Resolution via Deep Recursive Residual Network](http://openaccess.thecvf.com/content_cvpr_2017/papers/Tai_Image_Super-Resolution_via_CVPR_2017_paper.pdf)|[Matlab](https://github.com/tyshiwo/DRRN_CVPR17)|296
|2017|CVPRW|[Balanced two-stage residual networks for image super-resolution](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Fan_Balanced_Two-Stage_Residual_CVPR_2017_paper.pdf)|[Tensorflow](https://github.com/ychfan/sr_ntire2017)|18
|2017|CVPRW|[Ntire 2017 challenge on single image super-resolution: Methods and results](http://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Timofte_NTIRE_2017_Challenge_CVPR_2017_paper.pdf)|-|209
|2017|ICCV|[Enhancenet: Single image super-resolution through automated texture synthesis](http://openaccess.thecvf.com/content_ICCV_2017/papers/Sajjadi_EnhanceNet_Single_Image_ICCV_2017_paper.pdf)|-|167|
|2017|ICCV|[Pixel recursive super resolution](http://openaccess.thecvf.com/content_ICCV_2017/papers/Dahl_Pixel_Recursive_Super_ICCV_2017_paper.pdf)|-|96
|2017|ICCV|[Image super-resolution using dense skip connections](http://openaccess.thecvf.com/content_ICCV_2017/papers/Tong_Image_Super-Resolution_Using_ICCV_2017_paper.pdf)|-|130|
|2017|TIP|[Deep edge guided recurrent residual learning for image super-resolution](https://arxiv.org/pdf/1604.08671.pdf)|-|63
|2017|arxiv|[Srpgan: Perceptual generative adversarial network for single image super resolution](https://arxiv.org/pdf/1712.05927.pdf)|-|12|
|2017|ICONIP|[Fast and Accurate Image Super Resolution by Deep CNN with Skip Connection and Network in Network](https://arxiv.org/pdf/1707.05425.pdf)|[Tensorflow](https://github.com/jiny2001/dcscn-super-resolution)|43
|2016|CVPR|[Accurate image super-resolution using very deep convolutional networks](https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Kim_Accurate_Image_Super-Resolution_CVPR_2016_paper.pdf)|-|103
|2016|CVPR|[Deeply-recursive convolutional network for image super-resolution](http://openaccess.thecvf.com/content_cvpr_2016/papers/Kim_Deeply-Recursive_Convolutional_Network_CVPR_2016_paper.pdf)|-|551|
|2016|ECCV|[Accelerating the super-resolution convolutional neural network](https://arxiv.org/pdf/1608.00367.pdf)|-|493|
|2016|TIP|[Robust single image super-resolution via deep networks with sparse prior](http://www.ifp.illinois.edu/~dingliu2/iccv15/tip16.pdf)|-|124
|2015|TPAMI|[Image super-resolution using deep convolutional networks](https://arxiv.org/pdf/1501.00092.pdf)|-|1876
|2015|ICCV|[Deep Networks for Image Super-Resolution with Sparse Prior](http://openaccess.thecvf.com/content_iccv_2015/papers/Wang_Deep_Networks_for_ICCV_2015_paper.pdf)|-|373

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