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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.
FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
ICME 2025 Grand Challenge on Video Super-Resolution for Video Conferencing
LiftVSR: Lifting Image Diffusion to Video Super-Resolution via Hybrid Temporal Modeling with Only 4$\times$RTX 4090s
A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
DualX-VSR: Dual Axial Spatial$\times$Temporal Transformer for Real-World Video Super-Resolution without Motion Compensation
A Survey of Deep Learning Video Super-Resolution
Omnidirectional Video Super-Resolution using Deep Learning
UltraVSR: Achieving Ultra-Realistic Video Super-Resolution with Efficient One-Step Diffusion Space
DOVE: Efficient One-Step Diffusion Model for Real-World Video Super-Resolution
Blind Restoration of High-Resolution Ultrasound Video
Hunyuan-Game: Industrial-grade Intelligent Game Creation Model
GRNN:Recurrent Neural Network based on Ghost Features for Video Super-Resolution
Super-Resolution Generative Adversarial Networks based Video Enhancement
EvEnhancer: Empowering Effectiveness, Efficiency and Generalizability for Continuous Space-Time Video Super-Resolution with Events
Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution
RepNet-VSR: Reparameterizable Architecture for High-Fidelity Video Super-Resolution
Event-Enhanced Blurry Video Super-Resolution
FedVSR: Towards Model-Agnostic Federated Learning in Video Super-Resolution
Rethinking Video Super-Resolution: Towards Diffusion-Based Methods without Motion Alignment
Spatial Degradation-Aware and Temporal Consistent Diffusion Model for Compressed Video Super-Resolution
DC-VSR: Spatially and Temporally Consistent Video Super-Resolution with Video Diffusion Prior
Low Resource Video Super-resolution using Memory and Residual Deformable Convolutions
BF-STVSR: B-Splines and Fourier-Best Friends for High Fidelity Spatial-Temporal Video Super-Resolution
DiffVSR: Enhancing Real-World Video Super-Resolution with Diffusion Models for Advanced Visual Quality and Temporal Consistency
STAR: Spatial-Temporal Augmentation with Text-to-Video Models for Real-World Video Super-Resolution
Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content
Sequence Matters: Harnessing Video Models in 3D Super-Resolution
A Plug-and-Play Algorithm for 3D Video Super-Resolution of Single-Photon LiDAR data
RTSR: A Real-Time Super-Resolution Model for AV1 Compressed Content
360-Degree Video Super Resolution and Quality Enhancement Challenge: Methods and Results
Warped Diffusion: Solving Video Inverse Problems with Image Diffusion Models
Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
Quality Prediction of AI Generated Images and Videos: Emerging Trends and Opportunities
SeeClear: Semantic Distillation Enhances Pixel Condensation for Video Super-Resolution
AIM 2024 Challenge on Video Super-Resolution Quality Assessment: Methods and Results
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