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 871–880 of 3874 papers

TitleStatusHype
Solving Inverse Problems with Hybrid Deep Image Priors: the challenge of preventing overfittingCode1
Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image RestorationCode1
Face Hallucination via Split-Attention in Split-Attention NetworkCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 NetworkCode1
Attaining Real-Time Super-Resolution for Microscopic Images Using GANCode1
Unfolding the Alternating Optimization for Blind Super ResolutionCode1
Efficient Image Super-Resolution Using Pixel AttentionCode1
Interpretable Detail-Fidelity Attention Network for Single Image Super-ResolutionCode1
High-throughput molecular imaging via deep learning enabled Raman spectroscopyCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified