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 451–460 of 3874 papers

TitleStatusHype
AEROMamba: An efficient architecture for audio super-resolution using generative adversarial networks and state space modelsCode2
General Geospatial Inference with a Population Dynamics Foundation ModelCode3
SuperResolution Radar Gesture Recognitio—0
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-ResolutionCode1
Quasi-Newton OMP Approach for Super-Resolution Channel Estimation and Extrapolation—0
A Modular Conditional Diffusion Framework for Image Reconstruction—0
Electro-diffusive modeling and the role of spine geometry on action potential propagation in neurons—0
WeatherGFM: Learning A Weather Generalist Foundation Model via In-context Learning—0
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing—0
SynthSet: Generative Diffusion Model for Semantic Segmentation in Precision AgricultureCode0
Show:102550
← PrevPage 46 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified