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 2351–2360 of 3874 papers

TitleStatusHype
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection—0
Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results—0
Conditional Neural Video Coding with Spatial-Temporal Super-Resolution—0
Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems—0
Multi-Contrast Super-Resolution MRI Through a Progressive Network—0
Computational structured illumination for high-content fluorescent and phase microscopy—0
Multi-dimensional topological loss for cortical plate segmentation in fetal brain MRI—0
Computational resolution limit: a theory towards super-resolution—0
Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network—0
SGSR: Structure-Guided Multi-Contrast MRI Super-Resolution via Spatio-Frequency Co-Query Attention—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified