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 2311–2320 of 3874 papers

TitleStatusHype
Modeling Deformable Gradient Compositions for Single-Image Super-Resolution—0
Modeling the Trade-off of Privacy Preservation and Activity Recognition on Low-Resolution Images—0
Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution—0
Modelling Point Spread Function in Fluorescence Microscopy with a Sparse Combination of Gaussian Mixture: Trade-off between Accuracy and Efficiency—0
Model reduction for the material point method via an implicit neural representation of the deformation map—0
A Comparison of Super-Resolution and Nearest Neighbors Interpolation Applied to Object Detection on Satellite Data—0
Consistency Trajectory Matching for One-Step Generative Super-Resolution—0
ConsisSR: Delving Deep into Consistency in Diffusion-based Image Super-Resolution—0
Monitoring of Hermit Crabs Using drone-captured imagery and Deep Learning based Super-Resolution Reconstruction and Improved YOLOv8—0
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified