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. (liuzhen.pwd@gmail.com)
1. Video Super Resolution
ICCV 2021
- Peng Yi et al., Omniscient Video Super-Resolution, [pdf] [PyTorch].
- Yinxiao Li et al., COMISR: Compression-Informed Video Super-Resolution, [pdf] [Tersorflow].
- Jinshan Pan et al., Deep Blind Video Super-Resolution, [pdf] [PyTorch].
- Xi Yang et al., Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning Scheme, [pdf] [PyTorch].
CVPR 2021
- Kelvin C.K. Chan et al., BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond, [pdf] [PyTorch].
- Gang Xu et al., Temporal Modulation Network for Controllable Space-Time Video Super-Resolution, [pdf] [PyTorch].
- Zebu Xiao et al., Space-Time Distillation for Video Super-Resolution, [pdf].
- Yongcheng Jing et al., Turning Frequency to Resolution: Video Super-Resolution via Event Cameras, [pdf].
ECCV 2020
- Takashi Isobe et al., Video Super-Resolution with Recurrent Structure-Detail Network, [pdf].
- Wenbo Li et al., MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution, [pdf].
CVPR 2020
- Xiaoyu Xiang et al., Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution, [pdf] [PyTorch].
- Takashi Isobe et al., Video Super-Resolution With Temporal Group Attention, [pdf].
- Yapeng Tian et al., TDAN: Temporally-Deformable Alignment Network for Video Super-Resolution, [pdf]
CVPR 2019
- Muhammad Haris et al., Recurrent Back-Projection Network for Video Super-Resolution, [pdf] [PyTorch].
- Sheng Li et al., Fast Spatio-Temporal Residual Network for Video Super-Resolution, [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] [Tensorflow].
- Haochen Zhang et al., Two-Stream Action Recognition-Oriented Video Super-Resolution, [pdf] [Tensorflow & PyTorch].
CVPR 2018
- Younghyun Jo et al., Deep Video Super-Resolution Network Using Dynamic Upsampling Filters Without Explicit Motion Compensation, [pdf] [PyTorch (only test code)].
- Mehdi S. M. Sajjadi et al., Frame-Recurrent Video Super-Resolution, [pdf].
CVPR 2017
- Jose Caballero et al., Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation, [pdf].
ICCV 2017
- Ding Liu et al., Robust Video Super-Resolution With Learned Temporal Dynamics, [pdf].
- Xin Tao et al., Detail-Revealing Deep Video Super-Resolution, [pdf].
CVPR 2016
- Wenzhe Shi et al., Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, [pdf].
ICCV 2015
- Renjie Liao et al., Video Super-Resolution via Deep Draft-Ensemble Learning, [pdf].
2. Video Frame Interpolation
ICCV 2021
- Zhiyang Yu et al., Training Weakly Supervised Video Frame Interpolation with Events, [pdf] [PyTorch].
- Junheum Park et al., Asymmetric Bilateral Motion Estimation for Video Frame Interpolation, [pdf] [PyTorch].
- Hyeonjun Sim et al., XVFI: eXtreme Video Frame Interpolation, [pdf] [PyTorch].
CVPR 2021
- Tianyu Ding et al., CDFI: Compression-Driven Network Design for Frame Interpolation, [pdf] [PyTorch].
- Stepan Tulyakov et al., Time Lens: Event-Based Video Frame Interpolation, [pdf] [PyTorch]
ECCV 2020
- Junheum Park et al., BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation, [pdf]
CVPR 2020
- Simon Niklaus et al., Softmax Splatting for Video Frame Interpolation, [pdf]
- Hyeongmin Lee et al., AdaCoF: Adaptive Collaboration of Flows for Video Frame Interpolation, [pdf]
- Wang Shen et al., Blurry Video Frame Interpolation, [pdf]
- Shurui Gui et al., FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation, [pdf]
- Myungsub Choi et al., Scene-Adaptive Video Frame Interpolation via Meta-Learning, [pdf]
CVPR 2019
- Tomer Peleg et al., IM-Net for High Resolution Video Frame Interpolation, [pdf].
- Wenbo Bao et al., Depth-Aware Video Frame Interpolation, [pdf] [PyTorch].
- Liangzhe Yuan et al., Zoom-In-To-Check: Boosting Video Interpolation via Instance-Level Discrimination, [pdf].
ICCV 2019
- Fitsum A. Reda et al., Unsupervised Video Interpolation Using Cycle Consistency, [pdf].
CVPR 2018
- Simone Meyer et al., PhaseNet for Video Frame Interpolation, [pdf].
- Simon Niklaus et al., Context-Aware Synthesis for Video Frame Interpolation, [pdf].
- Huaizu Jiang et al., Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation, [pdf] [PyTorch].
ECCV 2018
- Chao-Yuan Wu et al., Video Compression through Image Interpolation, [pdf].
CVPR 2017
- Simon Niklaus et al., Video Frame Interpolation via Adaptive Convolution, [pdf]).
ICCV 2017
- Simon Niklaus et al., Video Frame Interpolation via Adaptive Separable Convolution, [pdf] [PyTorch].
- Ziwei Liu et al., Video Frame Synthesis using Deep Voxel Flow, [pdf].
CVPR 2015
- Simone Meyer et al., Phase-Based Frame Interpolation for Video, [pdf].
3. Video Deblurring
ICCV 2021
- Wei Shang et al., Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames, [pdf] [PyTorch].
- Senyou Deng et al., Multi-Scale Separable Network for Ultra-High-Definition Video Deblurring, [pdf].
CVPR 2021
- Maitreya Suin et al., Gated Spatio-Temporal Attention-Guided Video Deblurring, [pdf].
- Dongxu Li et al., ARVo: Learning All-Range Volumetric Correspondence for Video Deblurring, [pdf].
ECCV 2020
- Zhihang Zhong et al., Efficient Spatio-Temporal Recurrent Neural Network for Video Deblurring, [pdf]
- Songnan Lin et al., Learning Event-Driven Video Deblurring and Interpolation, [pdf]
CVPR 2020
- Jinshan Pan et al., Cascaded Deep Video Deblurring Using Temporal Sharpness Prior, [pdf]
CVPR 2019
- Seungjun Nah et al., Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring, [pdf].
ICCV 2019
- Shangchen Zhou et al., Spatio-Temporal Filter Adaptive Network for Video Deblurring, [pdf].
- Wenqi Ren et al., Face Video Deblurring Using 3D Facial Priors, [pdf].
CVPR 2017
- Shuochen Su et al., Deep Video Deblurring for Hand-Held Cameras, [pdf].
- Liyuan Pan et al., Simultaneous Stereo Video Deblurring and Scene Flow Estimation, [pdf].
ICCV 2017
- Wenqi Ren et al., Video Deblurring via Semantic Segmentation and Pixel-Wise Non-Linear Kernel, [pdf].
- Tae Hyun Kim et al., Online Video Deblurring via Dynamic Temporal Blending Network, [pdf].
ECCV 2016
- Anita Sellent et al., video deblurring, [pdf].
CVPR 2015
- Tae Hyun Kim et al., Generalized Video Deblurring for Dynamic Scenes, [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] [PyTorch].
- Dong Lao et al., Flow-Guided Video Inpainting With Scene Templates, [pdf].
- Bingyao Yu et al., Frequency-Aware Spatiotemporal Transformers for Video Inpainting Detection, [pdf].
- Hao Ouyang et al., Internal Video Inpainting by Implicit Long-Range Propagation, [pdf] [Tensorflow].
CVPR 2021
- Xueyan Zou et al., Progressive Temporal Feature Alignment Network for Video Inpainting, [pdf] [PyTorch].
ECCV 2020
- Ang Li et al., Short-Term and Long-Term Context Aggregation Network for Video Inpainting, [pdf]
- Yanhong Zeng et al., Learning Joint Spatial-Temporal Transformations for Video Inpainting, [pdf]
- Miao Liao et al., DVI: Depth Guided Video Inpainting for Autonomous Driving, [pdf]
CVPR 2019
- Rui Xu et al., Deep Flow-Guided Video Inpainting, [pdf].
- Dahun Kim et al., Deep Video Inpainting, [pdf].
ICCV 2019
- Haotian Zhang et al., An Internal Learning Approach to Video Inpainting, [pdf].
- Sungho Lee et al., Copy-and-Paste Networks for Deep Video Inpainting, [pdf].
- Ya-Liang Chang et al., Free-Form Video Inpainting With 3D Gated Convolution and Temporal PatchGAN, [pdf].
5. Video Denoising
ICCV 20221
- Gregory Vaksman et al., Patch Craft: Video Denoising by Deep Modeling and Patch Matching, [pdf].
- Dev Yashpal Sheth et al., Unsupervised Deep Video Denoising, [pdf] [PyTorch].
CVPR 2021
- Matteo Maggioni et al., Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion, [pdf] [PyTorch].
CVPR 2020
- Huanjing Yue et al., Supervised Raw Video Denoising With a Benchmark Dataset on Dynamic Scenes, [pdf]
- Matias Tassano et al., FastDVDnet: Towards Real-Time Deep Video Denoising Without Flow Estimation, [pdf]
CVPR 2019
- Thibaud Ehret et al., Model-Blind Video Denoising via Frame-To-Frame Training, [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].
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] [Matlab]
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] [Tensorflow]