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 2821–2830 of 3874 papers

TitleStatusHype
Edge and Identity Preserving Network for Face Super-ResolutionCode1
Multi-Dimension Fusion Network for Light Field Spatial Super-Resolution using Dynamic FiltersCode1
Deep Variational Network Toward Blind Image RestorationCode1
A Single Frame and Multi-Frame Joint Network for 360-degree Panorama Video Super-ResolutionCode1
Cascade Convolutional Neural Network for Image Super-Resolution—0
Exploit Camera Raw Data for Video Super-Resolution via Hidden Markov Model InferenceCode1
PNEN: Pyramid Non-Local Enhanced Networks—0
Biased Mixtures Of Experts: Enabling Computer Vision Inference Under Data Transfer Limitations—0
E-FCNN for tiny facial expression recognition—0
Single Image Super-Resolution via a Holistic Attention NetworkCode1
Show:102550
← PrevPage 283 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified