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 2111–2120 of 3874 papers

TitleStatusHype
Enhanced Image Reconstruction From Quarter Sampling Measurements Using An Adapted Very Deep Super Resolution Network—0
Spatio-temporal Vision Transformer for Super-resolution MicroscopyCode1
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—0
Conditional Simulation Using Diffusion Schrödinger BridgesCode1
Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution PriorsCode2
Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields TranslationCode2
Multi-image Super-resolution via Quality Map Associated Attention Network—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Super-resolution GANs of randomly-seeded fieldsCode0
Show:102550
← PrevPage 212 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified