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 2301–2310 of 3874 papers

TitleStatusHype
Global Stress Generation and Spatiotemporal Super-Resolution Physics-Informed Operator under Dynamic Loading for Two-Phase Random Materials—0
HALS: A Height-Aware Lidar Super-Resolution Framework for Autonomous Driving—0
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model—0
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models—0
G-PCC Post-Processing Using Fractional Super-Resolution—0
Gradient-Free Adversarial Purification with Diffusion Models—0
GRAM-HD: 3D-Consistent Image Generation at High Resolution with Generative Radiance Manifolds—0
GRAN: Ghost Residual Attention Network for Single Image Super Resolution—0
Graph Convolutional Networks in Feature Space for Image Deblurring and Super-resolution—0
Greedy Growing Enables High-Resolution Pixel-Based Diffusion Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified