SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 2051–2075 of 3874 papers

TitleStatusHype
Dynamic super-resolution in particle tracking problems—0
Dynamic Non-Regular Sampling Sensor Using Frequency Selective Reconstruction—0
Super-resolved multi-temporal segmentation with deep permutation-invariant networks—0
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts—0
Tracking Urbanization in Developing Regions with Remote Sensing Spatial-Temporal Super-Resolution—0
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder—0
Bayesian Image Super-Resolution with Deep Modeling of Image StatisticsCode1
MyStyle: A Personalized Generative Prior—0
Physics-informed deep-learning applications to experimental fluid mechanics—0
Cross-Modality High-Frequency Transformer for MR Image Super-Resolution—0
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
Reference-based Video Super-Resolution Using Multi-Camera Video TripletsCode2
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
Neural Vocoder is All You Need for Speech Super-resolutionCode1
Efficient and Degradation-Adaptive Network for Real-World Image Super-ResolutionCode1
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-ResolutionCode1
Learning Graph Regularisation for Guided Super-ResolutionCode1
Transformer-empowered Multi-scale Contextual Matching and Aggregation for Multi-contrast MRI Super-resolutionCode1
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows—0
MR Image Denoising and Super-Resolution Using Regularized Reverse Diffusion—0
Increasing the accuracy and resolution of precipitation forecasts using deep generative modelsCode1
Adaptive Patch Exiting for Scalable Single Image Super-ResolutionCode1
ARM: Any-Time Super-Resolution MethodCode1
Optical Flow for Video Super-Resolution: A Survey—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified