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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.
Scalable Neural Video Representations with Learnable Positional Features
Imagen Video: High Definition Video Generation with Diffusion Models
Real-RawVSR: Real-World Raw Video Super-Resolution with a Benchmark Dataset
ELSR: Extreme Low-Power Super Resolution Network For Mobile Devices
Sliding Window Recurrent Network for Efficient Video Super-Resolution
Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
Online Video Super-Resolution with Convolutional Kernel Bypass Graft
NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras
Towards Interpretable Video Super-Resolution via Alternating Optimization
Rethinking Alignment in Video Super-Resolution Transformers
Geometry-Aware Reference Synthesis for Multi-View Image Super-Resolution
Boosting Video Super Resolution with Patch-Based Temporal Redundancy Optimization
Enhancing Space-time Video Super-resolution via Spatial-temporal Feature Interaction
You Only Align Once: Bidirectional Interaction for Spatial-Temporal Video Super-Resolution
Deep Parametric 3D Filters for Joint Video Denoising and Illumination Enhancement in Video Super Resolution
Structured Sparsity Learning for Efficient Video Super-Resolution
AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos
VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
Recurrent Video Restoration Transformer with Guided Deformable Attention
Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restoration
Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
Spatial-Temporal Space Hand-in-Hand: Spatial-Temporal Video Super-Resolution via Cycle-Projected Mutual Learning
Accelerating the Training of Video Super-Resolution Models
None Class Ranking Loss for Document-Level Relation Extraction
Progressive Training of A Two-Stage Framework for Video Restoration
A New Dataset and Transformer for Stereoscopic Video Super-Resolution
Look Back and Forth: Video Super-Resolution with Explicit Temporal Difference Modeling
On the Generalization of BasicVSR++ to Video Deblurring and Denoising
Learning Trajectory-Aware Transformer for Video Super-Resolution
Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution
Optical Flow for Video Super-Resolution: A Survey
STDAN: Deformable Attention Network for Space-Time Video Super-Resolution
A Novel Dual Dense Connection Network for Video Super-resolution
Spatio-temporal Vision Transformer for Super-resolution Microscopy
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