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 2721–2730 of 3874 papers

TitleStatusHype
Protecting Intellectual Property of Generative Adversarial Networks From Ambiguity Attacks—0
Provable Compressed Sensing with Generative Priors via Langevin Dynamics—0
Unsupervised Alternating Optimization for Blind Hyperspectral Imagery Super-resolution—0
PINNs for Medical Image Analysis: A Survey—0
BLADE: Filter Learning for General Purpose Computational Photography—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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified