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 2411–2420 of 3874 papers

TitleStatusHype
IREM: High-Resolution Magnetic Resonance (MR) Image Reconstruction via Implicit Neural Representation—0
Multi-Modal Transformer for Accelerated MR ImagingCode1
"Zero-Shot" Point Cloud UpsamplingCode0
Sentinel-2 Sharpening Using a Single Unsupervised Convolutional Neural Network With MTF-Based Degradation ModelCode1
Video Super-Resolution with Long-Term Self-Exemplars—0
Advancing biological super-resolution microscopy through deep learning: a brief review—0
Fast Monte Carlo Rendering via Multi-Resolution SamplingCode1
Distilling the Knowledge from Conditional Normalizing FlowsCode0
Fairness for Image Generation with Uncertain Sensitive AttributesCode1
STRESS: Super-Resolution for Dynamic Fetal MRI using Self-Supervised LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified