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 3171–3180 of 3874 papers

TitleStatusHype
Using Physics-Informed Super-Resolution Generative Adversarial Networks for Subgrid Modeling in Turbulent Reactive Flows—0
Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-ResolutionCode0
Cascaded Detail-Preserving Networks for Super-Resolution of Document Images—0
Sub-frame Appearance and 6D Pose Estimation of Fast Moving ObjectsCode0
Deep Decomposition Learning for Inverse Imaging ProblemsCode0
Self-Enhanced Convolutional Network for Facial Video Hallucination—0
Joint Spatial and Angular Super-Resolution from a Single Image—0
PAG-Net: Progressive Attention Guided Depth Super-resolution Network—0
Single Image Super Resolution based on a Modified U-net with Mixed Gradient Loss—0
MetH: A family of high-resolution and variable-shape image challengesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified