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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 Blind Video Super-resolution
Deep Slow Motion Video Reconstruction with Hybrid Imaging System
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Video Face Super-Resolution with Motion-Adaptive Feedback Cell
Deep Video Super-Resolution using HR Optical Flow Estimation
End-To-End Trainable Video Super-Resolution Based on a New Mechanism for Implicit Motion Estimation and Compensation
Self-supervised Fine-tuning for Correcting Super-Resolution Convolutional Neural Networks
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal Loss
Sub-frame Appearance and 6D Pose Estimation of Fast Moving Objects
Self-Enhanced Convolutional Network for Facial Video Hallucination
Frame and Feature-Context Video Super-Resolution
Deformable Non-local Network for Video Super-Resolution
Robust Online Video Super-Resolution Using an Efficient Alternating Projections Scheme
A Single Video Super-Resolution GAN for Multiple Downsampling Operators based on Pseudo-Inverse Image Formation Models
Intra-frame Object Tracking by Deblatting
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Fast Spatio-Temporal Residual Network for Video Super-Resolution
Recurrent Back-Projection Network for Video Super-Resolution
Two-Stream Action Recognition-Oriented Video Super-Resolution
3DSRnet: Video Super-resolution using 3D Convolutional Neural Networks
TDAN: Temporally Deformable Alignment Network for Video Super-Resolution
Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
Learning for Video Super-Resolution through HR Optical Flow Estimation
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
Perceptual Video Super Resolution with Enhanced Temporal Consistency
Generative Adversarial Networks and Perceptual Losses for Video Super-Resolution
Deep Back-Projection Networks For Super-Resolution
Frame-Recurrent Video Super-Resolution
Learning a Single Convolutional Super-Resolution Network for Multiple Degradations
Video Enhancement with Task-Oriented Flow
End-to-End Learning of Video Super-Resolution with Motion Compensation
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations
Detail-revealing Deep Video Super-resolution
Multiframe Motion Coupling for Video Super Resolution
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