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 2511–2520 of 3874 papers

TitleStatusHype
Analog Neural Computing with Super-resolution Memristor Crossbars—0
An end-to-end Optical Character Recognition approach for ultra-low-resolution printed text images—0
Differentiable Neural Architecture Search for Extremely Lightweight Image Super-ResolutionCode1
Unsupervised Remote Sensing Super-Resolution via Migration Image PriorCode1
Infrared Image Super-Resolution via Transfer Learning and PSRGANCode1
Real-Time Video Super-Resolution by Joint Local Inference and Global Parameter Estimation—0
COMISR: Compression-Informed Video Super-ResolutionCode0
AI-assisted super-resolution cosmological simulations II: Halo substructures, velocities and higher order statistics—0
Brain Graph Super-Resolution Using Adversarial Graph Neural Network with Application to Functional Brain ConnectivityCode1
Simultaneous super-resolution and motion artifact removal in diffusion-weighted MRI using unsupervised deep learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified