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 2181–2190 of 3874 papers

TitleStatusHype
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionCode0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study—0
A Ray-tracing and Deep Learning Fusion Super-resolution Modeling Method for Wireless Mobile Channel—0
Multitemporal and multispectral data fusion for super-resolution of Sentinel-2 imagesCode0
Super-Resolution Analysis via Machine Learning: A Survey for Fluid Flows—0
Trainable Loss Weights in Super-ResolutionCode0
Self-FuseNet: Data Free Unsupervised Remote Sensing Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified