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 3041–3050 of 3874 papers

TitleStatusHype
Joint Face Completion and Super-resolution using Multi-scale Feature Relation Learning—0
Meta-Transfer Learning for Zero-Shot Super-ResolutionCode1
Learning Light Field Angular Super-Resolution via a Geometry-Aware NetworkCode1
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data—0
Unpaired Image Super-Resolution using Pseudo-SupervisionCode1
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-ResolutionCode1
RR-DnCNN v2.0: Enhanced Restoration-Reconstruction Deep Neural Network for Down-Sampling Based Video Coding—0
DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-ResolutionCode1
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud UpsamplingCode1
3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified