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 2941–2950 of 3874 papers

TitleStatusHype
Super-Resolution Domain Adaptation Networks for Semantic Segmentation via Pixel and Output Level AligningCode1
Invertible Image RescalingCode1
Scene Text Image Super-Resolution in the WildCode1
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode1
AIM 2019 Challenge on Video Temporal Super-Resolution: Methods and Results—0
Deep Generative Adversarial Residual Convolutional Networks for Real-World Super-ResolutionCode1
NTIRE 2020 Challenge on Perceptual Extreme Super-Resolution: Methods and Results—0
Joint-SRVDNet: Joint Super Resolution and Vehicle Detection Network—0
Neural Differential Equations for Single Image Super-resolution—0
On Solving SAR Imaging Inverse Problems Using Non-Convex Regularization with a Cauchy-based PenaltyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified