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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2491–2500 of 3874 papers

TitleStatusHype
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
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
HIME: Efficient Headshot Image Super-Resolution with Multiple Exemplars—0
A Survey of Super-Resolution in Iris Biometrics with Evaluation of Dictionary-Learning—0
NUNet: Deep Learning for Non-Uniform Super-Resolution of Turbulent Flows—0
Show:102550
← PrevPage 250 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified