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 3451–3460 of 3874 papers

TitleStatusHype
A Generative Model for Hallucinating Diverse Versions of Super Resolution Images—0
Supplementary Meta-Learning: Towards a Dynamic Model for Deep Neural Networks—0
Suppressing Uncertainties in Degradation Estimation for Blind Super-Resolution—0
A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts—0
Surf2CT: Cascaded 3D Flow Matching Models for Torso 3D CT Synthesis from Skin Surface—0
Surface Geometry Processing: An Efficient Normal-based Detail Representation—0
SURFNet: Super-resolution of Turbulent Flows with Transfer Learning using Small Datasets—0
Surveillance Face Anti-spoofing—0
A Generative Adversarial Network for AI-Aided Chair Design—0
A General Method to Incorporate Spatial Information into Loss Functions for GAN-based Super-resolution Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified