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 2521–2530 of 3874 papers

TitleStatusHype
Thermographic detection of internal defects using 2D photothermal super resolution reconstruction with sequential laser heating—0
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline—0
One-shot Ultra-high-Resolution Generative Adversarial Network That Synthesizes 16K Images On A Single GPU—0
Multi-image Super-resolution via Quality Map Associated Attention Network—0
Time Efficient Training of Progressive Generative Adversarial Network using Depthwise Separable Convolution and Super Resolution Generative Adversarial Network—0
Super-resolution GANs of randomly-seeded fieldsCode0
Rotationally Equivariant Super-Resolution of Velocity Fields in Two-Dimensional Fluids Using Convolutional Neural Networks—0
Mathematical Foundation of Sparsity-based Multi-snapshot Spectral Estimation—0
Disentangling Light Fields for Super-Resolution and Disparity Estimation—0
Autoencoding Low-Resolution MRI for Semantically Smooth Interpolation of Anisotropic MRI—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified