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)
- Kai Xu et al., Enhancing Video Super-Resolution via Implicit Resampling-based Alignment, [pdf] [PyTorch]
- Zhikai Chen et al., Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution, [pdf]
- Xingyu Zhou et al., Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention, [pdf] [[PyTorch]] (https://github.com/LabShuHangGU/MIA-VSR)
- Geunhyuk Youk et al., FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring, [pdf] [PyTorch]
- Shangchen Zhou et al., Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution, [pdf]
- Wei Shang et al., Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors, [pdf], [PyTorch]
- Claudio Rota et al., Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models, [pdf], [Soon]
- Ruicheng Feng et al., Kalman-Inspired Feature Propagation for Video Face Super-Resolution, [pdf], [PyTorch(test only)]
- Xi Yang et al., Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution, [pdf], [MMengine]
- Yuehan Zhang et al., RealViformer: Investigating Attention for Real-World Video Super-Resolution, [pdf], [Soon]
- Yuan Shen et al., SuperGaussian: Repurposing Video Models for 3D Super Resolution, [pdf], [Soon]
- Gen Li et al., Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting, [pdf] [PyTorch]
- Yingwei Wang et al., Compression-Aware Video Super-Resolution, [pdf] [PyTorch(test only)]
- Bin Xia et al., Structured Sparsity Learning for Efficient Video Super-Resolution, [pdf] [PyTorch]
- Yunfan Lu et al., Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution, [pdf] [PyTorch]
- Yi-Hsin Chen et al., MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution, [pdf], [PyTorch(test only)]
- Zixi Tuo et al., Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution, [pdf], [PyTorch]
- Jiyang Yu et al., Memory-Augmented Non-Local Attention for Video Super-Resolution, [pdf] [PyTorch]
- Zeyuan Chen et al., VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution, [pdf] [PyTorch]
- Kelvin C.K. Chan et al., BasicVSR++: Improving Video Super-Resolution With Enhanced Propagation and Alignment, [pdf] [MMengine]
- Zhicheng Geng et al., RSTT: Real-Time Spatial Temporal Transformer for Space-Time Video Super-Resolution, [pdf] [PyTorch]
- Chengxu Liu et al., Learning Trajectory-Aware Transformer for Video Super-Resolution, [pdf] [MMengine]
- Junyong Lee et al., Reference-Based Video Super-Resolution Using Multi-Camera Video Triplets, [pdf] [PyTorch]
- Kelvin C.K. Chan et al., Investigating Tradeoffs in Real-World Video Super-Resolution, [pdf] [MMengine]
- Zhongwei Qiu et al., Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution, [pdf], [PyTorch]
- Huanjing Yue et al., Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset, [pdf], [PyTorch]
- Jiezhang Cao et al., Towards Interpretable Video Super-Resolution via Alternating Optimization, [pdf], [PyTorch]
- 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].
- 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].
- 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].
- 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]
- 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].
- Xintao Wang et al., EDVR: Video Restoration with Enhanced Deformable Convolutional Networks, [pdf] [PyTorch]
- 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].
- 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].
- Jose Caballero et al., Real-Time Video Super-Resolution With Spatio-Temporal Networks and Motion Compensation, [pdf].
- Ding Liu et al., Robust Video Super-Resolution With Learned Temporal Dynamics, [pdf].
- Xin Tao et al., Detail-Revealing Deep Video Super-Resolution, [pdf].
- Wenzhe Shi et al., Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network, [pdf].
- Renjie Liao et al., Video Super-Resolution via Deep Draft-Ensemble Learning, [pdf].
- Guangyang Wu et al., Perception-Oriented Video Frame Interpolation via Asymmetric Blending, [pdf] [PyTorch]
- Chunxu Liu et al., Sparse Global Matching for Video Frame Interpolation with Large Motion, [pdf] [PyTorch]
- Zhihang Zhong et al., Clearer Frames, Anytime: Resolving Velocity Ambiguity in Video Frame Interpolation, [pdf], [PyTorch]
- Guozhen Zhang et al., Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation, [pdf] [PyTorch]
- Xin Jin et al., A Unified Pyramid Recurrent Network for Video Frame Interpolation, [pdf] [PyTorch]
- Taewoo Kim et al., Event-Based Video Frame Interpolation With Cross-Modal Asymmetric Bidirectional Motion Fields, [pdf] [PyTorch]
- Sangjin Lee et al., Exploring Discontinuity for Video Frame Interpolation, [pdf] [PyTorch(test only)]
- Wei Shang et al., Joint Video Multi-Frame Interpolation and Deblurring Under Unknown Exposure Time, [pdf], [PyTorch]
- Junheum Park et al., BiFormer: Learning Bilateral Motion Estimation via Bilateral Transformer for 4K Video Frame Interpolation, [pdf], [PyTorch]
- Xiang Ji et al., Rethinking Video Frame Interpolation from Shutter Mode Induced Degradation, [pdf], [PyTorch]
- Jun-Sang Yoo et al., Video Object Segmentation-aware Video Frame Interpolation, [pdf], [PyTorch]
- Xiao Lu et al., Video Shadow Detection via Spatio-Temporal Interpolation Consistency Training, [pdf], [PyTorch]
- Zhihao Shi et al., Video Frame Interpolation Transformer, [pdf], [PyTorch]
- Liying Lu et al., Video Frame Interpolation with Transformer, [pdf], [PyTorch]
- Yue Wu et al., Optimizing Video Prediction via Video Frame Interpolation, [pdf], [PyTorch]
- Ping Hu et al., Many-to-Many Splatting for Efficient Video Frame Interpolation, [pdf], [PyTorch]
- Zhewei Huang et al., Real-Time Intermediate Flow Estimation for Video Frame Interpolation, [pdf], [PyTorch]
- Fitsum Reda et al., FILM: Frame Interpolation for Large Motion, [pdf], [PyTorch]
- Zhiyang Yu et al., Deep Bayesian Video Frame Interpolation, [pdf], [PyTorch]
- Qiqi Hou et al., A Perceptual Quality Metric for Video Frame Interpolation, [pdf], [PyTorch]
- Jihyong Oh et al., DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting, [pdf], [PyTorch]
- 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].
- 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]
- Junheum Park et al., BMBC: Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation, [pdf]
- 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]
- 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].
- Fitsum A. Reda et al., Unsupervised Video Interpolation Using Cycle Consistency, [pdf].
- 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].
- Chao-Yuan Wu et al., Video Compression through Image Interpolation, [pdf].
- Simon Niklaus et al., Video Frame Interpolation via Adaptive Convolution, [pdf]).
- 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].
- Simone Meyer et al., Phase-Based Frame Interpolation for Video, [pdf].
- Huicong Zhang et al., Blur-aware Spatio-temporal Sparse Transformer for Video Deblurring, [pdf], [MMengine]
- Geunhyuk Youk et al., FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring, [pdf] [PyTorch]
- Jin-Ting He et al., Domain-adaptive Video Deblurring via Test-time Blurring, [pdf], [PyTorch(test only)]
- Taewoo Kim et al., Towards Real-world Event-guided Low-light Video Enhancement and Deblurring, [pdf], [Soon]
- Jinshan Pan et al., Deep Discriminative Spatial and Temporal Network for Efficient Video Deblurring, [pdf], [PyTorch]
- Wei Shang et al., Joint Video Multi-Frame Interpolation and Deblurring Under Unknown Exposure Time, [pdf], [PyTorch]
- Huicong Zhang et al., Spatio-Temporal Deformable Attention Network for Video Deblurring, [pdf], [PyTorch]
- Yusheng Wang et al., Efficient Video Deblurring Guided by Motion Magnitude, [pdf], [PyTorch]
- Bangrui Jiang et al., ERDN: Equivalent Receptive Field Deformable Network for Video Deblurring, [pdf], [PyTorch(test only)]
- Jihyong Oh et al., DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting, [pdf], [PyTorch]
- 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].
- 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].
- 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]
- Jinshan Pan et al., Cascaded Deep Video Deblurring Using Temporal Sharpness Prior, [pdf]
- Seungjun Nah et al., Recurrent Neural Networks With Intra-Frame Iterations for Video Deblurring, [pdf].
- 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].
- 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].
- 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].
- Anita Sellent et al., video deblurring, [pdf].
- Tae Hyun Kim et al., Generalized Video Deblurring for Dynamic Scenes, [pdf].
- Modeling Blurred Video with Layers, [pdf].
- Jianzong Wu et al., Towards Language-Driven Video Inpainting via Multimodal Large Language Models, [pdf], [PyTorch(test only)]
- Fu-Yun Wang et al., Be-Your-Outpainter: Mastering Video Outpainting through Input-Specific Adaptation, [pdf], [PyTorch(test only)]
- Shangchen Zhou et al., ProPainter: Improving Propagation and Transformer for Video Inpainting, [pdf], [PyTorch]
- Ryan Szeto et al., The DEVIL Is in the Details: A Diagnostic Evaluation Benchmark for Video Inpainting, [pdf], [PyTorch]
- Zhen Li et al., Towards An End-to-End Framework for Flow-Guided Video Inpainting, [pdf], [PyTorch]
- Jingjing Ren et al., DLFormer: Discrete Latent Transformer for Video Inpainting, [pdf], [PyTorch]
- Kaidong Zhang et al., Inertia-Guided Flow Completion and Style Fusion for Video Inpainting, [pdf], [PyTorch]
- 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].
- Xueyan Zou et al., Progressive Temporal Feature Alignment Network for Video Inpainting, [pdf] [PyTorch].
- 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]
- Rui Xu et al., Deep Flow-Guided Video Inpainting, [pdf].
- Dahun Kim et al., Deep Video Inpainting, [pdf].
- 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].
- Wenwen Pan et al., Wnet: Audio-Guided Video Object Segmentation via Wavelet-Based Cross-Modal Denoising Networks, [pdf], [PyTorch(test only)]
- Junyi Li et al., Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-Ahead Forward Ones, [pdf], [PyTorch]
- 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].
- Matteo Maggioni et al., Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion, [pdf] [PyTorch].
- 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]
- Thibaud Ehret et al., Model-Blind Video Denoising via Frame-To-Frame Training, [pdf].
- Bihan Wen et al., Joint Adaptive Sparsity and Low-Rankness on the Fly: An Online Tensor Reconstruction Scheme for Video Denoising, [pdf].
- Soo Ye Kim et al., Deep SR-ITM: Joint Learning of Super-Resolution and Inverse Tone-Mapping for 4K UHD HDR Applications, [pdf] [Matlab]
- 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]