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 3261–3270 of 3874 papers

TitleStatusHype
Lucas-Kanade Reloaded: End-to-End Super-Resolution from Raw Image Bursts—0
Algorithmic Hallucinations of Near-Surface Winds: Statistical Downscaling with Generative Adversarial Networks to Convection-Permitting Scales—0
Imitating the Functionality of Image-to-Image Models Using a Single Example—0
Video super-resolution for single-photon LIDAR—0
Steered Mixture-of-Experts Autoencoder Design for Real-Time Image Modelling and Denoising—0
Stereo Endoscopic Image Super-Resolution Using Disparity-Constrained Parallel Attention—0
Stereo Image Rain Removal via Dual-View Mutual Attention—0
StereoINR: Cross-View Geometry Consistent Stereo Super Resolution with Implicit Neural Representation—0
Stochastic Attribute Modeling for Face Super-Resolution—0
Stochastic Deep Restoration Priors for Imaging Inverse Problems—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified