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 2721–2730 of 3874 papers

TitleStatusHype
Can Super Resolution be used to improve Human Pose Estimation in Low Resolution Scenarios?—0
DeepCEL0 for 2D Single Molecule Localization in Fluorescence MicroscopyCode0
Blind Image Super-Resolution via Contrastive Representation Learning—0
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation—0
A Mixed-Supervision Multilevel GAN Framework for Image Quality Enhancement—0
"Zero-Shot" Point Cloud UpsamplingCode0
Advancing biological super-resolution microscopy through deep learning: a brief review—0
Video Super-Resolution with Long-Term Self-Exemplars—0
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Applying VertexShuffle Toward 360-Degree Video Super-Resolution on Focused-Icosahedral-Mesh—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified