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 3191–3200 of 3874 papers

TitleStatusHype
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
Degrees of freedom for off-the-grid sparse estimation—0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization—0
A deep learning framework for morphologic detail beyond the diffraction limit in infrared spectroscopic imagingCode0
Single-Frame Super-Resolution of Solar Magnetograms: Investigating Physics-Based Metrics \& Losses—0
Probabilistic Super-Resolution of Solar Magnetograms: Generating Many Explanations and Measuring Uncertainties—0
FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task LearningCode0
Cross-Scale Residual Network for Multiple Tasks:Image Super-resolution, Denoising, and Deblocking—0
AIM 2019 Challenge on Constrained Super-Resolution: Methods and ResultsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified