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 2851–2860 of 3874 papers

TitleStatusHype
Real-World Super-Resolution of Face-Images from Surveillance Cameras—0
Sampling Based Scene-Space Video Processing—0
Image Restoration by Deep Projected GSURE—0
Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledgeCode0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
An Interpretation of Regularization by Denoising and its Application with the Back-Projected Fidelity Term—0
Back-Projection Pipeline—0
Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolutionCode0
Quality Assessment of Super-Resolved Omnidirectional Image Quality Using Tangential Views—0
Circumventing the resolution-time tradeoff in Ultrasound Localization Microscopy by Velocity Filtering—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified