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Video Super-Resolution
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Video Super-Resolution is a computer vision task that aims to increase the resolution of a video sequence, typically from lower to higher resolutions. The goal is to generate high-resolution video frames from low-resolution input, improving the overall quality of the video.
<span style="color:grey; opacity: 0.6">( Image credit: Detail-revealing Deep Video Super-Resolution )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Deep Networks for Image and Video Super-Resolution
VRT: A Video Restoration Transformer
Self-Supervised Deep Blind Video Super-Resolution
Stable Long-Term Recurrent Video Super-Resolution
Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution
PP-MSVSR: Multi-Stage Video Super-Resolution
Revisiting Temporal Alignment for Video Restoration
Investigating Tradeoffs in Real-World Video Super-Resolution
AdaPool: Exponential Adaptive Pooling for Information-Retaining Downsampling
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution
ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
EFENet: Reference-based Video Super-Resolution with Enhanced Flow Estimation
Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution
An Efficient Network Design for Face Video Super-resolution
Memory-Augmented Non-Local Attention for Video Super-Resolution
SwinIR: Image Restoration Using Swin Transformer
Temporal Kernel Consistency for Blind Video Super-Resolution
Hierarchical Conditional Flow: A Unified Framework for Image Super-Resolution and Image Rescaling
Ada-VSR: Adaptive Video Super-Resolution with Meta-Learning
Self-Conditioned Probabilistic Learning of Video Rescaling
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data
Real-Time Super-Resolution System of 4K-Video Based on Deep Learning
Video Super-Resolution with Long-Term Self-Exemplars
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh
Group-based Bi-Directional Recurrent Wavelet Neural Networks for Video Super-Resolution
Video Super-Resolution Transformer
Real-Time Video Super-Resolution on Smartphones with Deep Learning, Mobile AI 2021 Challenge: Report
FDAN: Flow-guided Deformable Alignment Network for Video Super-Resolution
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation
COMISR: Compression-Informed Video Super-Resolution
NTIRE 2021 Challenge on Video Super-Resolution
BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
Temporal Modulation Network for Controllable Space-Time Video Super-Resolution
Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution
Omniscient Video Super-Resolution
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