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 3301–3310 of 3874 papers

TitleStatusHype
Medical image super-resolution method based on dense blended attention network—0
Zoom To Learn, Learn To ZoomCode1
Ensemble Super-Resolution with A Reference DatasetCode0
A Cone-Beam X-Ray CT Data Collection designed for Machine LearningCode0
Handheld Multi-Frame Super-ResolutionCode0
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
Adapting Image Super-Resolution State-of-the-arts and Learning Multi-model Ensemble for Video Super-Resolution—0
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution PipelineCode0
Face Hallucination by Attentive Sequence Optimization with Reinforcement Learning—0
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified