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 3501–3510 of 3874 papers

TitleStatusHype
Ensemble Learning for Microbubble Localization in Super-Resolution Ultrasound—0
Task-driven single-image super-resolution reconstruction of document scans—0
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training—0
EPSR: Edge Profile Super resolution—0
Equilibrium Conserving Neural Operators for Super-Resolution Learning—0
Enhancing digital core image resolution using optimal upscaling algorithm: with application to paired SEM images—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Enhancing Diffusion-Weighted Images (DWI) for Diffusion MRI: Is it Enough without Non-Diffusion-Weighted B=0 Reference?—0
Show:102550
← PrevPage 351 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified