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 2111–2120 of 3874 papers

TitleStatusHype
Enhancing Traffic Scene Predictions with Generative Adversarial Networks—0
Enhancing Weather Predictions: Super-Resolution via Deep Diffusion Models—0
Ensemble Learning for Microbubble Localization in Super-Resolution Ultrasound—0
EPNet: An Efficient Pyramid Network for Enhanced Single-Image Super-Resolution with Reduced Computational Requirements—0
EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training—0
EPSR: Edge Profile Super resolution—0
Equilibrium Conserving Neural Operators for Super-Resolution Learning—0
ERNet Family: Hardware-Oriented CNN Models for Computational Imaging Using Block-Based Inference—0
Error-correcting neural networks for semi-Lagrangian advection in the level-set method—0
ESC-MISR: Enhancing Spatial Correlations for Multi-Image Super-Resolution in Remote Sensing—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified