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 1161–1170 of 3874 papers

TitleStatusHype
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving TransformerCode0
Deep learning for temporal super-resolution 4D Flow MRICode0
Deep Learning for Single Image Super-Resolution: A Brief ReviewCode0
Deep Learning for Multiple-Image Super-ResolutionCode0
MetH: A family of high-resolution and variable-shape image challengesCode0
MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-MixerCode0
MR Slice Profile Estimation by Learning to Match Internal Patch DistributionsCode0
Deep Learning for Image Super-resolution: A SurveyCode0
Algorithmic Guarantees for Inverse Imaging with Untrained Network PriorsCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Show:102550
← PrevPage 117 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified