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 561–570 of 3874 papers

TitleStatusHype
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
FB-HyDON: Parameter-Efficient Physics-Informed Operator Learning of Complex PDEs via Hypernetwork and Finite Basis Domain Decomposition—0
Think Twice Before You Act: Improving Inverse Problem Solving With MCMC—0
Test-time Training for Hyperspectral Image Super-resolution—0
Learned Compression for Images and Point CloudsCode1
Mesh-based Super-Resolution of Fluid Flows with Multiscale Graph Neural Networks—0
Three-Dimensional, Multimodal Synchrotron Data for Machine Learning ApplicationsCode0
CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer—0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified