Video Enhancement
39 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Video Enhancement
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
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BVI-DVC
LDV
Vident-lab
Most implemented papers
Blurry Video Frame Interpolation
laomao0/BIN
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CVPR 2020
Existing works reduce motion blur and up-convert frame rate through two separate ways, including frame deblurring and frame interpolation.
Space-Time-Aware Multi-Resolution Video Enhancement
alterzero/STARnet
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CVPR 2020
We consider the problem of space-time super-resolution (ST-SR): increasing spatial resolution of video frames and simultaneously interpolating frames to increase the frame rate.
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.
CVEGAN: A Perceptually-inspired GAN for Compressed Video Enhancement
We propose a new Generative Adversarial Network for Compressed Video quality Enhancement (CVEGAN).
STAR: A Structure-Aware Lightweight Transformer for Real-Time Image Enhancement
zzyfd/STAR-pytorch
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ICCV 2021
STAR is a general architecture that can be easily adapted to different image enhancement tasks.
Seeing Dynamic Scene in the Dark: A High-Quality Video Dataset With Mechatronic Alignment
dvlab-research/sdsd
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ICCV 2021
Low-light video enhancement is an important task.
Learning Temporal Consistency for Low Light Video Enhancement From Single Images
zkawfanx/StableLLVE
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CVPR 2021
Based on this idea, we propose our method which can infer motion prior for single image low light video enhancement and enforce temporal consistency.
Patch-Wise Spatial-Temporal Quality Enhancement for HEVC Compressed Video
To overcome these limitations, we propose a patch-wise spatial-temporal quality enhancement network which firstly extracts spatial and temporal features, then recalibrates and fuses the obtained spatial and temporal features.
Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement
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Firstly, we design an enhancement factor extraction network using depthwise separable convolution for an efficient estimate of the pixel-wise light deficiency of an low-light image.
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).
