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 3361–3370 of 3874 papers

TitleStatusHype
Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN—0
Super Resolution image reconstructs via total variation-based image deconvolution: a majorization-minimization approach—0
Super-resolution imaging using super-oscillatory diffractive neural networks—0
Super-resolution in disordered media using neural networks—0
A hybrid approach of interpolations and CNN to obtain super-resolution—0
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks—0
Super-resolution meets machine learning: approximation of measures—0
Super-resolution Method for Coherent DOA Estimation of Multiple Wideband Sources—0
Superresolution method for data deconvolution by superposition of point sources—0
Show:102550
← PrevPage 337 of 388Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified