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 3181–3190 of 3874 papers

TitleStatusHype
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Proximal Splitting Networks for Image Restoration—0
PS^2F: Polarized Spiral Point Spread Function for Single-Shot 3D Sensing—0
PSRFlow: Probabilistic Super Resolution with Flow-Based Models for Scientific Data—0
PSyCo: Manifold Span Reduction for Super Resolution—0
PTSR: Patch Translator for Image Super-Resolution—0
Pyramidal Denoising Diffusion Probabilistic Models—0
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution—0
Pyramidal Edge-maps and Attention based Guided Thermal Super-resolution—0
Pyramid Dual Domain Injection Network for Pan-sharpening—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified