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 3141–3150 of 3874 papers

TitleStatusHype
Frequency Separation for Real-World Super-ResolutionCode0
Fine-Grained Neural Architecture Search—0
AIM 2019 Challenge on Real-World Image Super-Resolution: Methods and ResultsCode0
Multi-modal Deep Guided Filtering for Comprehensible Medical Image Processing—0
Towards the Automation of Deep Image Prior—0
Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair—0
Neutron Ghost Imaging—0
Natural and Realistic Single Image Super-Resolution with Explicit Natural Manifold DiscriminationCode0
Perception-oriented Single Image Super-Resolution via Dual Relativistic Average Generative Adversarial Networks—0
Degrees of freedom for off-the-grid sparse estimation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified