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 2601–2610 of 3874 papers

TitleStatusHype
High-Resolution WiFi Imaging with Reconfigurable Intelligent Surfaces—0
Conditional Generation Using Polynomial ExpansionsCode0
SwiftSRGAN -- Rethinking Super-Resolution for Efficient and Real-time Inference—0
Performance of a GPU- and Time-Efficient Pseudo 3D Network for Magnetic Resonance Image Super-Resolution and Motion Artifact Reduction—0
Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution—0
A Close Look at Few-shot Real Image Super-resolution from the Distortion Relation Perspective—0
Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform—0
AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified