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 2631–2640 of 3874 papers

TitleStatusHype
Functional Neural Networks for Parametric Image Restoration Problems—0
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through timeCode0
MEGAN: Memory Enhanced Graph Attention Network for Space-Time Video Super-Resolution—0
Localized Super Resolution for Foreground Images using U-Net and MR-CNNCode0
RBSRICNN: Raw Burst Super-Resolution through Iterative Convolutional Neural NetworkCode0
Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution—0
Dense Dual-Attention Network for Light Field Image Super-Resolution—0
Multimodal-Boost: Multimodal Medical Image Super-Resolution using Multi-Attention Network with Wavelet Transform—0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution—0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified