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 1821–1830 of 3874 papers

TitleStatusHype
Fast and Accurate Image Upscaling With Super-Resolution Forests—0
Implicit neural representations for unsupervised super-resolution and denoising of 4D flow MRI—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network—0
Kalman-Inspired Feature Propagation for Video Face Super-Resolution—0
A Scale-Arbitrary Image Super-Resolution Network Using Frequency-domain Information—0
AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions—0
Joint Multiple FMCW Chirp Sequence Processing for Velocity Estimation and Ambiguity Resolving—0
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms—0
FAST: A Framework to Accelerate Super-Resolution Processing on Compressed Videos—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified