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 511–520 of 3874 papers

TitleStatusHype
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real WorldCode1
A Practical Contrastive Learning Framework for Single-Image Super-ResolutionCode1
Manifold Matching via Deep Metric Learning for Generative ModelingCode1
Face Hallucination via Split-Attention in Split-Attention NetworkCode1
Face Super-Resolution Guided by 3D Facial PriorsCode1
Context-self contrastive pretraining for crop type semantic segmentationCode1
Adversarial Magnification to Deceive Deepfake Detection through Super ResolutionCode1
Convolutional Neural Network Modelling for MODIS Land Surface Temperature Super-ResolutionCode1
3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstructionCode1
Consistent Direct Time-of-Flight Video Depth Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified