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 601–610 of 3874 papers

TitleStatusHype
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
A Physics-Informed Meta-Learning Framework for the Continuous Solution of Parametric PDEs on Arbitrary GeometriesCode1
Deep Plug-and-Play Prior for Hyperspectral Image RestorationCode1
edge-SR: Super-Resolution For The MassesCode1
CNN-generated images are surprisingly easy to spot... for nowCode1
EDPN: Enhanced Deep Pyramid Network for Blurry Image RestorationCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified