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 2611–2620 of 3874 papers

TitleStatusHype
Selfie Periocular Verification using an Efficient Super-Resolution Approach—0
Intermediate Layer Optimization for Inverse Problems using Deep Generative ModelsCode1
Multi-Texture GAN: Exploring the Multi-Scale Texture Translation for Brain MR Images—0
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Reference-based Texture transfer for Single Image Super-resolution of Magnetic Resonance imagesCode0
Deep learning architectural designs for super-resolution of noisy imagesCode1
Single Image Super-Resolution using Residual Channel Attention NetworkCode0
Protecting Intellectual Property of Generative Adversarial Networks from Ambiguity AttackCode1
I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling—0
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Show:102550
← PrevPage 262 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified