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 3241–3250 of 3874 papers

TitleStatusHype
Recurrent Structure Attention Guidance for Depth Super-Resolution—0
Recurrent Super-Resolution Method for Enhancing Low Quality Thermal Facial Data—0
Recursive Deep Prior Video: a Super Resolution algorithm for Time-Lapse Microscopy of organ-on-chip experiments—0
Redefining Automotive Radar Imaging: A Domain-Informed 1D Deep Learning Approach for High-Resolution and Efficient Performance—0
Redefining Neural Operators in d+1 Dimensions—0
Redefining Super-Resolution: Fine-mesh PDE predictions without classical simulations—0
Reducing Magnetic Resonance Image Spacing by Learning Without Ground-Truth—0
Reference-based OCT Angiogram Super-resolution with Learnable Texture Generation—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
Reference-Free Image Quality Metric for Degradation and Reconstruction Artifacts—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified