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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.
AIM 2024 Challenge on Efficient Video Super-Resolution for AV1 Compressed Content
Cascaded Temporal Updating Network for Efficient Video Super-Resolution
Kalman-Inspired Feature Propagation for Video Face Super-Resolution
3DAttGAN: A 3D Attention-based Generative Adversarial Network for Joint Space-Time Video Super-Resolution
Cuboid-Net: A Multi-Branch Convolutional Neural Network for Joint Space-Time Video Super Resolution
RealViformer: Investigating Attention for Real-World Video Super-Resolution
Arbitrary-Scale Video Super-Resolution with Structural and Textural Priors
Global Spatial-Temporal Information-based Residual ConvLSTM for Video Space-Time Super-Resolution
VEnhancer: Generative Space-Time Enhancement for Video Generation
DaBiT: Depth and Blur informed Transformer for Joint Refocusing and Super-Resolution
Improving Generative Adversarial Networks for Video Super-Resolution
EvTexture: Event-driven Texture Enhancement for Video Super-Resolution
HR-INR: Continuous Space-Time Video Super-Resolution via Event Camera
Towards Real-world Video Face Restoration: A New Benchmark
VideoGigaGAN: Towards Detail-rich Video Super-Resolution
Space-Time Video Super-resolution with Neural Operator
Collaborative Feedback Discriminative Propagation for Video Super-Resolution
Translation-based Video-to-Video Synthesis
Learning Spatial Adaptation and Temporal Coherence in Diffusion Models for Video Super-Resolution
Time-series Initialization and Conditioning for Video-agnostic Stabilization of Video Super-Resolution using Recurrent Networks
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
Deep Blind Super-Resolution for Satellite Video
Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
FMA-Net: Flow-Guided Dynamic Filtering and Iterative Feature Refinement with Multi-Attention for Joint Video Super-Resolution and Deblurring
A Survey on Super Resolution for video Enhancement Using GAN
TMP: Temporal Motion Propagation for Online Video Super-Resolution
Semantic Lens: Instance-Centric Semantic Alignment for Video Super-Resolution
Upscale-A-Video: Temporal-Consistent Diffusion Model for Real-World Video Super-Resolution
Photorealistic Video Generation with Diffusion Models
Motion-Guided Latent Diffusion for Temporally Consistent Real-world Video Super-resolution
Hypergraph Contrastive Learning for Drug Trafficking Community Detection
Enhancing Perceptual Quality in Video Super-Resolution through Temporally-Consistent Detail Synthesis using Diffusion Models
FLAIR: A Conditional Diffusion Framework with Applications to Face Video Restoration
RBPGAN: Recurrent Back-Projection GAN for Video Super Resolution
A Lightweight Recurrent Aggregation Network for Satellite Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.