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 2391–2400 of 3874 papers

TitleStatusHype
Multi-modality super-resolution loss for GAN-based super-resolution of clinical CT images using micro CT image database—0
Compressed Video Super-Resolution based on Hierarchical Encoding—0
A Comparative Study of Feature Expansion Unit for 3D Point Cloud Upsampling—0
Multi-Modal Super Resolution for Dense Microscopic Particle Size Estimation—0
A comparative analysis of SRGAN models—0
Multiple Angles of Arrival Estimation using Neural Networks—0
Multiple Exemplars-based Hallucinationfor Face Super-resolution and Editing—0
Multi-Reference Image Super-Resolution: A Posterior Fusion Approach—0
Turbulence Enrichment using Physics-informed Generative Adversarial Networks—0
Multi-resolution Data Fusion for Super-Resolution Electron Microscopy—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified