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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3511–3520 of 3874 papers

TitleStatusHype
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
Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements—0
Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling—0
ESRPCB: an Edge guided Super-Resolution model and Ensemble learning for tiny Printed Circuit Board Defect detection—0
Enhancing Building Semantic Segmentation Accuracy with Super Resolution and Deep Learning: Investigating the Impact of Spatial Resolution on Various Datasets—0
ESSR: An 8K@30FPS Super-Resolution Accelerator With Edge Selective Network—0
ESTformer: Transformer Utilizing Spatiotemporal Dependencies for Electroencaphalogram Super-resolution—0
Estimates of maize plant density from UAV RGB images using Faster-RCNN detection model: impact of the spatial resolution—0
ESTISR: Adapting Efficient Scene Text Image Super-resolution for Real-Scenes—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified