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 3291–3300 of 3874 papers

TitleStatusHype
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data—0
Fully Convolutional Network for Removing DCT Artefacts From Images—0
FC^2N: Fully Channel-Concatenated Network for Single Image Super-ResolutionCode0
Multi-level Wavelet Convolutional Neural NetworksCode0
MRI Super-Resolution with Ensemble Learning and Complementary Priors—0
Improving the resolution of microscope by deconvolution after dense scan—0
Distilling with Residual Network for Single Image Super Resolution—0
Deep learning in ultrasound imaging—0
Super-Resolution of PROBA-V Images Using Convolutional Neural Networks—0
Tranquil Clouds: Neural Networks for Learning Temporally Coherent Features in Point Clouds—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified