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 2581–2590 of 3874 papers

TitleStatusHype
Can Location Embeddings Enhance Super-Resolution of Satellite Imagery?—0
PanFlowNet: A Flow-Based Deep Network for Pan-sharpening—0
CANDID: Correspondence AligNment for Deep-burst Image Denoising—0
Uncertainty Quantification via Neural Posterior Principal Components—0
Panoramas from Photons—0
Can Deep Learning Relax Endomicroscopy Hardware Miniaturization Requirements?—0
PAON: A New Neuron Model using Padé Approximants—0
ParaDiS: Parallelly Distributable Slimmable Neural Networks—0
Calcium oscillation on homogeneous and heterogeneous networks of ryanodine receptor—0
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified