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 841–850 of 3874 papers

TitleStatusHype
Learning Structral coherence Via Generative Adversarial Network for Single Image Super-ResolutionCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face RecognitionCode1
Hyperspectral Image Super-Resolution with Spectral Mixup and Heterogeneous DatasetsCode1
Fast and accurate learned multiresolution dynamical downscaling for precipitationCode1
DeFlow: Learning Complex Image Degradations from Unpaired Data with Conditional FlowsCode1
Deep Learning-based Face Super-Resolution: A SurveyCode1
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learningCode1
Real-World Video Super-Resolution: A Benchmark Dataset and a Decomposition Based Learning SchemeCode1
Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGANCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified