Video Restoration
26 papers with code • 0 benchmarks • 5 datasets
Benchmarks
These leaderboards are used to track progress in Video Restoration
TAPE
BS-RSC
Vident-lab
Most implemented papers
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
xinntao/EDVR
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In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges.
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
open-mmlab/mmediting
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CVPR 2022
We show that by empowering the recurrent framework with the enhanced propagation and alignment, one can exploit spatiotemporal information across misaligned video frames more effectively.
Recurrent Video Restoration Transformer with Guided Deformable Attention
Specifically, RVRT divides the video into multiple clips and uses the previously inferred clip feature to estimate the subsequent clip feature.
Progressive Training of A Two-Stage Framework for Video Restoration
As a widely studied task, video restoration aims to enhance the quality of the videos with multiple potential degradations, such as noises, blurs and compression artifacts.
Reference-based Restoration of Digitized Analog Videotapes
miccunifi/tape
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We design a transformer-based Swin-UNet network that exploits both neighboring and reference frames via our Multi-Reference Spatial Feature Fusion (MRSFF) blocks.
MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video
Finally, experiments validate the effectiveness and generalization ability of our MFQE approach in advancing the state-of-the-art quality enhancement of compressed video.
Spatio-temporal deformable convolution for compressed video quality enhancement
Recent years have witnessed remarkable success of deep learning methods in quality enhancement for compressed video.
Influence-guided Data Augmentation for Neural Tensor Completion
srijankr/dain
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In this paper, we propose DAIN, a general data augmentation framework that enhances the prediction accuracy of neural tensor completion methods.
DeMFI: Deep Joint Deblurring and Multi-Frame Interpolation with Flow-Guided Attentive Correlation and Recursive Boosting
JihyongOh/DeMFI
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In this paper, we propose a novel joint deblurring and multi-frame interpolation (DeMFI) framework, called DeMFI-Net, which accurately converts blurry videos of lower-frame-rate to sharp videos at higher-frame-rate based on flow-guided attentive-correlation-based feature bolstering (FAC-FB) module and recursive boosting (RB), in terms of multi-frame interpolation (MFI).
Revisiting Temporal Alignment for Video Restoration
redrock303/revisiting-temporal-alignment-for-video-restoration
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CVPR 2022
Long-range temporal alignment is critical yet challenging for video restoration tasks.
