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 3681–3690 of 3874 papers

TitleStatusHype
Low Resolution Face Recognition Using a Two-Branch Deep Convolutional Neural Network Architecture—0
Multi-frame image super-resolution with fast upscaling technique—0
DOTE: Dual cOnvolutional filTer lEarning for Super-Resolution and Cross-Modality Synthesis in MRI—0
A New Adaptive Video Super-Resolution Algorithm With Improved Robustness to Innovations—0
Recurrent Inference Machines for Solving Inverse ProblemsCode1
Deep Learning for Isotropic Super-Resolution from Non-Isotropic 3D Electron Microscopy—0
Adversarial Inverse Graphics Networks: Learning 2D-to-3D Lifting and Image-to-Image Translation from Unpaired Supervision—0
Computation-Performance Optimization of Convolutional Neural Networks with Redundant Kernel RemovalCode0
LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled VariablesCode0
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified