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https://github.com/liuzhen03/awesome-video-enhancement

Paper list for video enhancement, including video super-resolution, interpolation, denoising, deblurring and inpainting.
https://github.com/liuzhen03/awesome-video-enhancement
List: awesome-video-enhancement
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Paper list for video enhancement, including video super-resolution, interpolation, denoising, deblurring and inpainting.
Host: GitHub
URL: https://github.com/liuzhen03/awesome-video-enhancement
Owner: liuzhen03
Created: 2019-11-20T08:29:11.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2022-06-02T08:58:40.000Z (over 2 years ago)
Last Synced: 2024-05-21T04:01:04.593Z (6 months ago)
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Open Issues: 0
Metadata Files:
- Readme: README.md
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README

        # Awesome Video Enhancement

Paper list for video enhancement, including video super-resolution, interpolation, denoising, deblurring and inpainting.

By Zhen Liu. If you have any suggestions, please email me. ([email protected]) 

## 1. Video Super Resolution

### ICCV 2021

* Peng Yi et al., **Omniscient Video Super-Resolution**, [[pdf]](https://arxiv.org/abs/2103.15683) [[PyTorch]](https://github.com/psychopa4/OVSR).

* Yinxiao Li et al., **COMISR: Compression-Informed Video Super-Resolution**, [[pdf]](https://arxiv.org/abs/2105.01237) [[Tersorflow]](https://github.com/google-research/google-research/tree/master/comisr).

* Jinshan Pan et al., **Deep Blind Video Super-Resolution**, [[pdf]](https://arxiv.org/abs/2003.04716) [[PyTorch]](https://github.com/csbhr/Deep-Blind-VSR).

* Xi Yang et al., **Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme**, [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yang_Real-World_Video_Super-Resolution_A_Benchmark_Dataset_and_a_Decomposition_Based_ICCV_2021_paper.pdf) [[PyTorch]](https://github.com/IanYeung/RealVSR).

### CVPR 2021

* Kelvin C.K. Chan et al., **BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond**, [[pdf]](https://arxiv.org/abs/2012.02181) [[PyTorch]](https://github.com/ckkelvinchan/BasicVSR-IconVSR).

* Gang Xu et al., **Temporal Modulation Network for Controllable Space-Time Video Super-Resolution**, [[pdf]](https://arxiv.org/abs/2104.10642) [[PyTorch]](https://github.com/CS-GangXu/TMNet).

* Zebu Xiao et al., **Space-Time Distillation for Video Super-Resolution**, [[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Xiao_Space-Time_Distillation_for_Video_Super-Resolution_CVPR_2021_paper.pdf).

* Yongcheng Jing et al., **Turning Frequency to Resolution: Video Super-Resolution via Event Cameras**, [[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Jing_Turning_Frequency_to_Resolution_Video_Super-Resolution_via_Event_Cameras_CVPR_2021_paper.pdf).

### ECCV 2020

* Takashi Isobe et al., **Video Super-Resolution with Recurrent Structure-Detail Network**, [[pdf]](https://arxiv.org/pdf/2008.00455).

* Wenbo Li et al., **MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution**, [[pdf\]](https://arxiv.org/pdf/2007.11803).

### CVPR 2020

* Xiaoyu Xiang et al., **Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Xiang_Zooming_Slow-Mo_Fast_and_Accurate_One-Stage_Space-Time_Video_Super-Resolution_CVPR_2020_paper.pdf) [[PyTorch\]]().

* Takashi Isobe et al., **Video Super-Resolution With Temporal Group Attention**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Isobe_Video_Super-Resolution_With_Temporal_Group_Attention_CVPR_2020_paper.pdf).

* Yapeng Tian et al., **TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution**,  [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Tian_TDAN_Temporally-Deformable_Alignment_Network_for_Video_Super-Resolution_CVPR_2020_paper.pdf)

### CVPR 2019

* Muhammad Haris et al., **Recurrent Back-Projection Network for Video Super-Resolution**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Haris_Recurrent_Back-Projection_Network_for_Video_Super-Resolution_CVPR_2019_paper.pdf) [[PyTorch\]]().

* Sheng Li et al., **Fast Spatio-Temporal Residual Network for Video Super-Resolution**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Li_Fast_Spatio-Temporal_Residual_Network_for_Video_Super-Resolution_CVPR_2019_paper.pdf).

### CVPRW 2019

* Xintao Wang et al., **EDVR: Video Restoration with Enhanced Deformable Convolutional Networks**, [[pdf\]]() [[PyTorch\]]()

### ICCV 2019

* Peng Yi et al., **Progressive Fusion Video Super-Resolution Network via Exploiting Non-Local Spatio-Temporal Correlations**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Yi_Progressive_Fusion_Video_Super-Resolution_Network_via_Exploiting_Non-Local_Spatio-Temporal_Correlations_ICCV_2019_paper.pdf) [[Tensorflow\]]().

* Haochen Zhang et al., **Two-Stream Action Recognition-Oriented Video Super-Resolution**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_Two-Stream_Action_Recognition-Oriented_Video_Super-Resolution_ICCV_2019_paper.pdf) [[Tensorflow & PyTorch\]]().

### CVPR 2018

* Younghyun Jo et al., **Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jo_Deep_Video_Super-Resolution_CVPR_2018_paper.pdf) [[PyTorch (only test code)\]]().

* Mehdi S. M. Sajjadi et al., **Frame-Recurrent Video Super-Resolution**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Sajjadi_Frame-Recurrent_Video_Super-Resolution_CVPR_2018_paper.pdf).

### CVPR 2017

* Jose Caballero et al., **Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Caballero_Real-Time_Video_Super-Resolution_CVPR_2017_paper.pdf).

### ICCV 2017

* Ding Liu et al., **Robust Video Super-Resolution With Learned Temporal Dynamics**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Robust_Video_Super-Resolution_ICCV_2017_paper.pdf).

* Xin Tao et al., **Detail-Revealing Deep Video Super-Resolution**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Tao_Detail-Revealing_Deep_Video_ICCV_2017_paper.pdf).

### CVPR 2016

* Wenzhe Shi et al., **Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf).

### ICCV 2015

* Renjie Liao et al., **Video Super-Resolution via Deep Draft-Ensemble Learning**, [[pdf\]](http://openaccess.thecvf.com/content_iccv_2015/papers/Liao_Video_Super-Resolution_via_ICCV_2015_paper.pdf).

## 2. Video Frame Interpolation

### ICCV 2021

* Zhiyang Yu et al., **Training Weakly Supervised Video Frame Interpolation with Events**, [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/html/Yu_Training_Weakly_Supervised_Video_Frame_Interpolation_With_Events_ICCV_2021_paper.html) [[PyTorch]](https://github.com/YU-Zhiyang/WEVI).

* Junheum Park et al., **Asymmetric Bilateral Motion Estimation for Video Frame Interpolation**, [[pdf]](https://arxiv.org/abs/2108.06815) [[PyTorch]](https://github.com/JunHeum/ABME).

* Hyeonjun Sim et al., **XVFI: eXtreme Video Frame Interpolation**, [[pdf]](http://arxiv.org/abs/2103.16206) [[PyTorch]](https://github.com/JihyongOh/XVFI).

### CVPR 2021

* Tianyu Ding et al., **CDFI: Compression-Driven Network Design for Frame Interpolation**, [[pdf]](https://arxiv.org/abs/2103.10559) [[PyTorch]](https://github.com/tding1/CDFI).

* Stepan Tulyakov et al., **Time Lens: Event-Based Video Frame Interpolation**, [[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Tulyakov_Time_Lens_Event-Based_Video_Frame_Interpolation_CVPR_2021_paper.pdf) [[PyTorch]](https://github.com/uzh-rpg/rpg_timelens)

### ECCV 2020

* Junheum Park et al., **BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation**, [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123590103.pdf)

### CVPR 2020

* Simon Niklaus et al., **Softmax Splatting for Video Frame Interpolation**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Niklaus_Softmax_Splatting_for_Video_Frame_Interpolation_CVPR_2020_paper.pdf)

* Hyeongmin Lee et al., **AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_AdaCoF_Adaptive_Collaboration_of_Flows_for_Video_Frame_Interpolation_CVPR_2020_paper.pdf)

* Wang Shen et al., **Blurry Video Frame Interpolation**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Shen_Blurry_Video_Frame_Interpolation_CVPR_2020_paper.pdf)

* Shurui Gui et al., **FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation**,  [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Gui_FeatureFlow_Robust_Video_Interpolation_via_Structure-to-Texture_Generation_CVPR_2020_paper.pdf)

* Myungsub Choi et al., **Scene-Adaptive Video Frame Interpolation via Meta-Learning**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Choi_Scene-Adaptive_Video_Frame_Interpolation_via_Meta-Learning_CVPR_2020_paper.pdf)

### CVPR 2019

* Tomer Peleg et al., **IM-Net for High Resolution Video Frame Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Peleg_IM-Net_for_High_Resolution_Video_Frame_Interpolation_CVPR_2019_paper.pdf).

* Wenbo Bao et al., **Depth-Aware Video Frame Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Bao_Depth-Aware_Video_Frame_Interpolation_CVPR_2019_paper.pdf) [[PyTorch\]]().

* Liangzhe Yuan et al., **Zoom-In-To-Check: Boosting Video Interpolation via Instance-Level Discrimination**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Yuan_Zoom-In-To-Check_Boosting_Video_Interpolation_via_Instance-Level_Discrimination_CVPR_2019_paper.pdf).

### ICCV 2019

* Fitsum A. Reda et al., **Unsupervised Video Interpolation Using Cycle Consistency**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Reda_Unsupervised_Video_Interpolation_Using_Cycle_Consistency_ICCV_2019_paper.pdf).

### CVPR 2018

* Simone Meyer et al., **PhaseNet for Video Frame Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Meyer_PhaseNet_for_Video_CVPR_2018_paper.pdf).

* Simon Niklaus et al., **Context-Aware Synthesis for Video Frame Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Niklaus_Context-Aware_Synthesis_for_CVPR_2018_paper.pdf).

* Huaizu Jiang et al., **Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2018/papers/Jiang_Super_SloMo_High_CVPR_2018_paper.pdf) [[PyTorch\]]().

### ECCV 2018

* Chao-Yuan Wu et al., **Video Compression through Image Interpolation**, [[pdf\]](http://openaccess.thecvf.com/content_ECCV_2018/papers/Chao-Yuan_Wu_Video_Compression_through_ECCV_2018_paper.pdf).

### CVPR 2017

* Simon Niklaus et al., **Video Frame Interpolation via Adaptive Convolution**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Niklaus_Video_Frame_Interpolation_CVPR_2017_paper.pdf)).

### ICCV 2017

* Simon Niklaus et al., **Video Frame Interpolation via Adaptive Separable Convolution**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Niklaus_Video_Frame_Interpolation_ICCV_2017_paper.pdf) [[PyTorch\]]().

* Ziwei Liu et al., **Video Frame Synthesis using Deep Voxel Flow**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Liu_Video_Frame_Synthesis_ICCV_2017_paper.pdf).

### CVPR 2015

* Simone Meyer et al., **Phase-Based Frame Interpolation for Video**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2015/papers/Meyer_Phase-Based_Frame_Interpolation_2015_CVPR_paper.pdf).

## 3. Video Deblurring

### ICCV 2021

* Wei Shang et al., **Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames**, [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Shang_Bringing_Events_Into_Video_Deblurring_With_Non-Consecutively_Blurry_Frames_ICCV_2021_paper.pdf) [[PyTorch]](https://github.com/shangwei5/D2Net).

* Senyou Deng et al., **Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring**, [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Deng_Multi-Scale_Separable_Network_for_Ultra-High-Definition_Video_Deblurring_ICCV_2021_paper.pdf).

### CVPR 2021

* Maitreya Suin et al., **Gated Spatio-Temporal Attention-Guided Video Deblurring**, [[pdf]](https://openaccess.thecvf.com/content/CVPR2021/papers/Suin_Gated_Spatio-Temporal_Attention-Guided_Video_Deblurring_CVPR_2021_paper.pdf).

* Dongxu Li et al., **ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring**, [[pdf]](https://arxiv.org/abs/2103.04260).

### ECCV 2020

* Zhihang Zhong et al., **Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring**, [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123510188.pdf)

* Songnan Lin et al., **Learning Event-Driven Video Deblurring and Interpolation**, [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123530681.pdf)

### CVPR 2020

* Jinshan Pan et al., **Cascaded Deep Video Deblurring Using Temporal Sharpness Prior**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Pan_Cascaded_Deep_Video_Deblurring_Using_Temporal_Sharpness_Prior_CVPR_2020_paper.pdf)

### CVPR 2019

* Seungjun Nah et al., **Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Nah_Recurrent_Neural_Networks_With_Intra-Frame_Iterations_for_Video_Deblurring_CVPR_2019_paper.pdf).

### ICCV 2019

* Shangchen Zhou et al., **Spatio-Temporal Filter Adaptive Network for Video Deblurring**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Spatio-Temporal_Filter_Adaptive_Network_for_Video_Deblurring_ICCV_2019_paper.pdf).

* Wenqi Ren et al., **Face Video Deblurring Using 3D Facial Priors**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Ren_Face_Video_Deblurring_Using_3D_Facial_Priors_ICCV_2019_paper.pdf).

### CVPR 2017

* Shuochen Su et al., **Deep Video Deblurring for Hand-Held Cameras**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Su_Deep_Video_Deblurring_CVPR_2017_paper.pdf).

* Liyuan Pan et al., **Simultaneous Stereo Video Deblurring and Scene Flow Estimation**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Pan_Simultaneous_Stereo_Video_CVPR_2017_paper.pdf).

### ICCV 2017

* Wenqi Ren et al., **Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Ren_Video_Deblurring_via_ICCV_2017_paper.pdf).

* Tae Hyun Kim et al., **Online Video Deblurring via Dynamic Temporal Blending Network**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Kim_Online_Video_Deblurring_ICCV_2017_paper.pdf).

### ECCV 2016

* Anita Sellent et al., **video deblurring**, [[pdf\]]().

### CVPR 2015

* Tae Hyun Kim et al., **Generalized Video Deblurring for Dynamic Scenes**, [[pdf\]](http://openaccess.thecvf.com/content_cvpr_2015/papers/Kim_Generalized_Video_Deblurring_2015_CVPR_paper.pdf).

### ECCV 2014

* **Modeling Blurred Video with Layers**, [[pdf\]]().

## 4. Video Inpainting

### ICCV 2021

* Rui Liu et al., **FuseFormer: Fusing Fine-Grained Information in Transformers for Video Inpainting**, [[pdf]](https://arxiv.org/abs/2109.02974) [[PyTorch]](https://github.com/ruiliu-ai/FuseFormer).

* Dong Lao et al., **Flow-Guided Video Inpainting With Scene Templates**, [[pdf]](https://arxiv.org/abs/2108.12845).

* Bingyao Yu et al., **Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection**, [[pdf]](https://openaccess.thecvf.com/content/ICCV2021/papers/Yu_Frequency-Aware_Spatiotemporal_Transformers_for_Video_Inpainting_Detection_ICCV_2021_paper.pdf).

* Hao Ouyang et al., **Internal Video Inpainting by Implicit Long-Range Propagation**, [[pdf]](https://arxiv.org/abs/2108.01912) [[Tensorflow]](https://github.com/Tengfei-Wang/Implicit-Internal-Video-Inpainting).

### CVPR 2021

* Xueyan Zou et al., **Progressive Temporal Feature Alignment Network for Video Inpainting**, [[pdf]](https://arxiv.org/abs/2104.03507) [[PyTorch]](https://github.com/MaureenZOU/TSAM).

### ECCV 2020

* Ang Li et al., **Short-Term and Long-Term Context Aggregation Network for Video Inpainting**, [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123490698.pdf)

* Yanhong Zeng et al., **Learning Joint Spatial-Temporal Transformations for Video Inpainting**,  [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123610511.pdf)

* Miao Liao et al., **DVI: Depth Guided Video Inpainting for Autonomous Driving**, [[pdf\]](https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123660001.pdf)

### CVPR 2019

* Rui Xu et al., **Deep Flow-Guided Video Inpainting**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Deep_Flow-Guided_Video_Inpainting_CVPR_2019_paper.pdf).

* Dahun Kim et al., **Deep Video Inpainting**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Kim_Deep_Video_Inpainting_CVPR_2019_paper.pdf).

### ICCV 2019

* Haotian Zhang et al., **An Internal Learning Approach to Video Inpainting**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhang_An_Internal_Learning_Approach_to_Video_Inpainting_ICCV_2019_paper.pdf).

* Sungho Lee et al., **Copy-and-Paste Networks for Deep Video Inpainting**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Lee_Copy-and-Paste_Networks_for_Deep_Video_Inpainting_ICCV_2019_paper.pdf).

* Ya-Liang Chang et al., **Free-Form Video Inpainting With 3D Gated Convolution and Temporal PatchGAN**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2019/papers/Chang_Free-Form_Video_Inpainting_With_3D_Gated_Convolution_and_Temporal_PatchGAN_ICCV_2019_paper.pdf).

## 5. Video Denoising

### ICCV 20221

* Gregory Vaksman et al., **Patch Craft: Video Denoising by Deep Modeling and Patch Matching**, [[pdf]](https://arxiv.org/abs/2103.13767).

* Dev Yashpal Sheth et al., **Unsupervised Deep Video Denoising**, [[pdf]](https://arxiv.org/abs/2011.15045) [[PyTorch]](https://github.com/sreyas-mohan/udvd).

### CVPR 2021

* Matteo Maggioni et al., **Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion**, [[pdf]](https://arxiv.org/abs/2103.05407) [[PyTorch]](https://github.com/Baymax-chen/EMVD).

### CVPR 2020

* Huanjing Yue et al., **Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Yue_Supervised_Raw_Video_Denoising_With_a_Benchmark_Dataset_on_Dynamic_CVPR_2020_paper.pdf)

* Matias Tassano et al., **FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation**, [[pdf\]](https://openaccess.thecvf.com/content_CVPR_2020/papers/Tassano_FastDVDnet_Towards_Real-Time_Deep_Video_Denoising_Without_Flow_Estimation_CVPR_2020_paper.pdf)

### CVPR 2019

- Thibaud Ehret et al., **Model-Blind Video Denoising via Frame-To-Frame Training**, [[pdf\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Ehret_Model-Blind_Video_Denoising_via_Frame-To-Frame_Training_CVPR_2019_paper.pdf).

### ICCV 2017

- Bihan Wen et al., **Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising**, [[pdf\]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Wen_Joint_Adaptive_Sparsity_ICCV_2017_paper.pdf).

## 6. Video HDR（Inverse Tone-Mapping）

### ICCV 2019

* Soo Ye Kim et al., **Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications**,  [[pdf\]](https://arxiv.org/pdf/1904.11176)  [[Matlab\]](https://github.com/sooyekim/Deep-SR-ITM)

### AAAI 2019

* Soo Ye Kim et al., **JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Speciﬁc Filters for UHD HDR Video**, [[pdf\]](https://arxiv.org/abs/1909.04391) [[Tensorflow\]](https://github.com/JihyongOh/JSI-GAN)