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 1571–1580 of 3874 papers

TitleStatusHype
An Unsupervised Framework for Joint MRI Super Resolution and Gibbs Artifact Removal—0
A statistically constrained internal method for single image super-resolution—0
Benchmarking Probabilistic Deep Learning Methods for License Plate RecognitionCode0
Energy-Inspired Self-Supervised Pretraining for Vision Models—0
An Operator Theory for Analyzing the Resolution of Multi-illumination Imaging Modalities—0
Millimetre-wave Radar for Low-Cost 3D Imaging: A Performance Study—0
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Structure Flow-Guided Network for Real Depth Super-Resolution—0
Accelerating Guided Diffusion Sampling with Splitting Numerical MethodsCode1
Image Restoration with Mean-Reverting Stochastic Differential EquationsCode2
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified