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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.
Large Motion Video Super-Resolution with Dual Subnet and Multi-Stage Communicated Upsampling
Self-Supervised Adaptation for Video Super-Resolution
Learning for Unconstrained Space-Time Video Super-Resolution
Deep Unrolled Network for Video Super-Resolution
Towards Real-World Blind Face Restoration with Generative Facial Prior
Deep Learning Techniques for Super-Resolution in Video Games
BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
EVRNet: Efficient Video Restoration on Edge Devices
DeFMO: Deblurring and Shape Recovery of Fast Moving Objects
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments
DynaVSR: Dynamic Adaptive Blind Video Super-Resolution
Real-time Surgical Environment Enhancement for Robot-Assisted Minimally Invasive Surgery Based on Super-Resolution
Deformable Kernel Convolutional Network for Video Extreme Super-Resolution
Local-Global Fusion Network for Video Super-Resolution
NEMO: enabling neural-enhanced video streaming on commodity mobile devices
Understanding Deformable Alignment in Video Super-Resolution
AIM 2020 Challenge on Video Extreme Super-Resolution: Methods and Results
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-Resolution
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model Inference
Revisiting Temporal Modeling for Video Super-resolution
Blind Face Restoration via Deep Multi-scale Component Dictionaries
Video Super-Resolution with Recurrent Structure-Detail Network
Neural supersampling for real-time rendering
Video Super Resolution Based on Deep Learning: A Comprehensive Survey
MuCAN: Multi-Correspondence Aggregation Network for Video Super-Resolution
Video Super-resolution with Temporal Group Attention
Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks
iSeeBetter: Spatio-Temporal Video Super Resolution using Recurrent-Generative Back-Projection Networks
Real-World Super-Resolution via Kernel Estimation and Noise Injection
Efficient and Phase-aware Video Super-resolution for Cardiac MRI
Deep Space-Time Video Upsampling Networks
Deformable 3D Convolution for Video Super-Resolution
Space-Time-Aware Multi-Resolution Video Enhancement
Across Scales & Across Dimensions: Temporal Super-Resolution using Deep Internal Learning
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