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 2551–2560 of 3874 papers

TitleStatusHype
Perceptual cGAN for MRI Super-resolutionCode0
A Review of Deep Learning Based Image Super-resolution Techniques—0
Robust Unpaired Single Image Super-Resolution of Faces—0
SparseAlign: A Super-Resolution Algorithm for Automatic Marker Localization and Deformation Estimation in Cryo-Electron Tomography—0
Enhancement or Super-Resolution: Learning-based Adaptive Video Streaming with Client-Side Video Processing—0
Virtual Coil Augmentation Technology for MR Coil Extrapolation via Deep Learning—0
Dual Perceptual Loss for Single Image Super-Resolution Using ESRGAN—0
Improving Clinical Diagnosis Performance with Automated X-ray Scan Quality Enhancement Algorithms—0
CISRNet: Compressed Image Super-Resolution NetworkCode0
UDC: Unified DNAS for Compressible TinyML Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified