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 1511–1520 of 3874 papers

TitleStatusHype
Low Complexity DoA-ToA Signature Estimation for Multi-Antenna Multi-Carrier Systems—0
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
Lightweight single-image super-resolution network based on dual paths—0
EDADepth: Enhanced Data Augmentation for Monocular Depth EstimationCode0
Distilling Generative-Discriminative Representations for Very Low-Resolution Face Recognition—0
Single-snapshot machine learning for super-resolution of turbulence—0
Empirical Bayesian image restoration by Langevin sampling with a denoising diffusion implicit prior—0
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization ProblemCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified