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
Deep Recurrent Neural Network with Multi-scale Bi-directional Propagation for Video Deblurring
xjtu-cvlab-lowlevel/rnn-mbp
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Instead of estimating alignment information, we propose a simple and effective deep Recurrent Neural Network with Multi-scale Bi-directional Propagation (RNN-MBP) to effectively propagate and gather the information from unaligned neighboring frames for better video deblurring.
Transcoded Video Restoration by Temporal Spatial Auxiliary Network
In most video platforms, such as Youtube, and TikTok, the played videos usually have undergone multiple video encodings such as hardware encoding by recording devices, software encoding by video editing apps, and single/multiple video transcoding by video application servers.
Self-Supervised Deep Blind Video Super-Resolution
As directly using LR videos as supervision usually leads to trivial solutions, we develop a simple and effective method to generate auxiliary paired data from original LR videos according to the image formation of video SR, so that the networks can be better constrained by the generated paired data for both blur kernel estimation and latent HR video restoration.
VRT: A Video Restoration Transformer
jingyunliang/vrt
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Besides, parallel warping is used to further fuse information from neighboring frames by parallel feature warping.
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
ckkelvinchan/basicvsr_plusplus
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The exploitation of long-term information has been a long-standing problem in video restoration.
Unidirectional Video Denoising by Mimicking Backward Recurrent Modules with Look-ahead Forward Ones
nagejacob/flornn
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However, BiRNN is intrinsically offline because it uses backward recurrent modules to propagate from the last to current frames, which causes high latency and large memory consumption.
VDTR: Video Deblurring with Transformer
ljzycmd/vdtr
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For multi-frame temporal modeling, we adapt Transformer to fuse multiple spatial features efficiently.
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
On the other hand, we equip the sequence-to-sequence model with an unsupervised optical flow estimator to maximize its potential.
A Simple Baseline for Video Restoration with Grouped Spatial-temporal Shift
dasongli1/shift-net
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CVPR 2023
In this study, we propose a simple yet effective framework for video restoration.
Restoration of User Videos Shared on Social Media
This paper presents a new general video restoration framework for the restoration of user videos shared on social media platforms.
