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

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Showing 2891–2900 of 3874 papers

TitleStatusHype
Augmented Equivariant Attention Networks for Microscopy Image Reconstruction—0
Resistance-Time Co-Modulated PointNet for Temporal Super-Resolution Simulation of Blood Vessel Flows—0
Resolution- and Stimulus-agnostic Super-Resolution of Ultra-High-Field Functional MRI: Application to Visual Studies—0
Resolution based Feature Distillation for Cross Resolution Person Re-Identification—0
Resolution enhancement in scanning electron microscopy using deep learning—0
Resolution Enhancement of Scanning Electron Micrographs using Artificial Intelligence—0
Resolution Invariant Autoencoder—0
Unsupervised Super-Resolution: Creating High-Resolution Medical Images from Low-Resolution Anisotropic Examples—0
AUDIT: Audio Editing by Following Instructions with Latent Diffusion Models—0
Unsupervised Super-Resolution of Satellite Imagery for High Fidelity Material Label Transfer—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified