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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.
VCISR: Blind Single Image Super-Resolution with Video Compression Synthetic Data
Scale-Adaptive Feature Aggregation for Efficient Space-Time Video Super-Resolution
HSTR-Net: Reference Based Video Super-resolution with Dual Cameras
Video Super-Resolution Using a Grouped Residual in Residual Network
LAVIE: High-Quality Video Generation with Cascaded Latent Diffusion Models
A Lightweight Recurrent Grouping Attention Network for Video Super-Resolution
SimDA: Simple Diffusion Adapter for Efficient Video Generation
CycMuNet+: Cycle-Projected Mutual Learning for Spatial-Temporal Video Super-Resolution
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
RefVSR++: Exploiting Reference Inputs for Reference-based Video Super-resolution
Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR
NegVSR: Augmenting Negatives for Generalized Noise Modeling in Real-World Video Super-Resolution
EgoVSR: Towards High-Quality Egocentric Video Super-Resolution
Can SAM Boost Video Super-Resolution?
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution
Enhancing Video Super-Resolution via Implicit Resampling-based Alignment
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
Local-Global Temporal Difference Learning for Satellite Video Super-Resolution
Learning Spatial-Temporal Implicit Neural Representations for Event-Guided Video Super-Resolution
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
Online Streaming Video Super-Resolution with Convolutional Look-Up Table
Continuous Space-Time Video Super-Resolution Utilizing Long-Range Temporal Information
In-situ monitoring additive manufacturing process with AI edge computing
Cross-Resolution Flow Propagation for Foveated Video Super-Resolution
Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution
Reference-based Image and Video Super-Resolution via C2-Matching
Mitigating Artifacts in Real-World Video Super-Resolution Models
Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report
Temporal Consistency Learning of inter-frames for Video Super-Resolution
Video super-resolution for single-photon LIDAR
A Codec Information Assisted Framework for Efficient Compressed Video Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.