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 3801–3810 of 3874 papers

TitleStatusHype
Deep Learning-based Image Super-Resolution Considering Quantitative and Perceptual QualityCode0
Flexible Alignment Super-Resolution Network for Multi-Contrast MRICode0
A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial ImageryCode0
Deep Learning-Based Channel EstimationCode0
FLAIR: A Conditional Diffusion Framework with Applications to Face Video RestorationCode0
Calorimeter shower superresolutionCode0
Continual Learning Approaches for Anomaly DetectionCode0
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation NetworksCode0
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
Content and Colour Distillation for Learning Image Translations with the Spatial Profile LossCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified