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 3601–3610 of 3874 papers

TitleStatusHype
cGANs with Projection DiscriminatorCode0
Learning from a Handful Volumes: MRI Resolution Enhancement with Volumetric Super-Resolution ForestsCode0
Image Transformer—0
Quantifying Uncertainty in Discrete-Continuous and Skewed Data with Bayesian Deep LearningCode0
Deep Image Super Resolution via Natural Image Priors—0
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor—0
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid FlowCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified