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 1411–1420 of 3874 papers

TitleStatusHype
GCRDN: Global Context-Driven Residual Dense Network for Remote Sensing Image SuperresolutionCode1
Unsupervised Domain Adaptation for Neuron Membrane Segmentation based on Structural Features—0
Expanding Synthetic Real-World Degradations for Blind Video Super Resolution—0
An FCNN-Based Super-Resolution mmWave Radar Framework for Contactless Musical Instrument InterfaceCode0
Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution NetworkCode2
Deep Learning-Based Multiband Signal Fusion for 3-D SAR Super-ResolutionCode1
A Vision Transformer Approach for Efficient Near-Field Irregular SAR Super-ResolutionCode1
Efficient CNN-based Super Resolution Algorithms for mmWave Mobile Radar ImagingCode1
Deep Learning-Assisted Simultaneous Targets Sensing and Super-Resolution Imaging—0
Self-supervised arbitrary scale super-resolution framework for anisotropic MRI—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified