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 3841–3850 of 3874 papers

TitleStatusHype
Super-resolution MRI Using Finite Rate of Innovation Curves—0
Image Super-Resolution Using Deep Convolutional NetworksCode1
First order algorithms in variational image processing—0
Fast Sublinear Sparse Representation using Shallow Tree Matching Pursuit—0
Higher-order MRFs based image super resolution: why not MAP?—0
Super-resolution method using sparse regularization for point-spread function recovery—0
Single Image Super Resolution via Manifold Approximation—0
A fast patch-dictionary method for whole image recovery—0
A New Approach for Super resolution by Using Web Images and FFT Based Image Registration—0
Generative Adversarial NetworksCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified