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 461–470 of 3874 papers

TitleStatusHype
A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net DiscriminatorsCode1
Diffusion Prior Interpolation for Flexibility Real-World Face Super-ResolutionCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Enhanced Super-Resolution Training via Mimicked Alignment for Real-World ScenesCode1
Conffusion: Confidence Intervals for Diffusion ModelsCode1
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
CVAE-GAN: Fine-Grained Image Generation through Asymmetric TrainingCode1
DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRFCode1
Distillation-Driven Diffusion Model for Multi-Scale MRI Super-Resolution: Make 1.5T MRI Great AgainCode1
Enhancement of Anime Imaging Enlargement using Modified Super-Resolution CNNCode1
Show:102550
← PrevPage 47 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified