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 3071–3080 of 3874 papers

TitleStatusHype
Seven ways to improve example-based single image super resolution—0
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network—0
Sex-Classification from Cell-Phones Periocular Iris Images—0
SGDFormer: One-stage Transformer-based Architecture for Cross-Spectral Stereo Image Guided Denoising—0
AnyEnhance: A Unified Generative Model with Prompt-Guidance and Self-Critic for Voice Enhancement—0
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal—0
Verification of Very Low-Resolution Faces Using An Identity-Preserving Deep Face Super-Resolution Network—0
A Nuclear-norm Model for Multi-Frame Super-Resolution Reconstruction from Video Clips—0
ShipSRDet: An End-to-End Remote Sensing Ship Detector Using Super-Resolved Feature Representation—0
A Novel Fast 3D Single Image Super-Resolution Algorithm—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified