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 2381–2390 of 3874 papers

TitleStatusHype
Self-Conditioned Probabilistic Learning of Video RescalingCode1
Compressed Ultrasound Imaging:from Sub-Nyquist Rates to Super-Resolution—0
Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic DataCode4
High-Resolution Pelvic MRI Reconstruction Using a Generative Adversarial Network with Attention and Cyclic Loss—0
RankSRGAN: Super Resolution Generative Adversarial Networks with Learning to Rank—0
Joint Implicit Image Function for Guided Depth Super-ResolutionCode1
ProfileSR-GAN: A GAN based Super-Resolution Method for Generating High-Resolution Load Profiles—0
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation—0
Level generation and style enhancement -- deep learning for game development overview—0
Multi-Attention Generative Adversarial Network for Remote Sensing Image Super-Resolution—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified