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 2041–2050 of 3874 papers

TitleStatusHype
EECD-Net: Energy-Efficient Crack Detection with Spiking Neural Networks and Gated Attention—0
E-FCNN for tiny facial expression recognition—0
Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction—0
Effective Invertible Arbitrary Image Rescaling—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
Effectiveness of State-of-the-Art Super Resolution Algorithms in Surveillance Environment—0
Effectivity of super resolution convolutional neural network for the enhancement of land cover classification from medium resolution satellite images—0
Effect of structure-based training on 3D localization precision and quality—0
Effect of Super Resolution on High Dimensional Features for Unsupervised Face Recognition in the Wild—0
Effects of Data Enrichment with Image Transformations on the Performance of Deep Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified