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 561–570 of 3874 papers

TitleStatusHype
Deep Space-Time Video Upsampling NetworksCode1
Degradation Oriented and Regularized Network for Blind Depth Super-ResolutionCode1
Deep Posterior Distribution-based Embedding for Hyperspectral Image Super-resolutionCode1
Deep learning of multi-resolution X-Ray micro-CT images for multi-scale modellingCode1
Catch-A-Waveform: Learning to Generate Audio from a Single Short ExampleCode1
Deep Reparametrization of Multi-Frame Super-Resolution and DenoisingCode1
Deep Learning for Efficient Reconstruction of High-Resolution Turbulent DNS DataCode1
Deep learning techniques for blind image super-resolution: A high-scale multi-domain perspective evaluationCode1
Deep Unfolding Network for Image Super-ResolutionCode1
Angular Super-Resolution in Diffusion MRI with a 3D Recurrent Convolutional AutoencoderCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified