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 2051–2060 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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified