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 3331–3340 of 3874 papers

TitleStatusHype
Residual Networks for Light Field Image Super-Resolution—0
Zoom to Learn, Learn to Zoom—0
Generative Imaging and Image Processing via Generative Encoder—0
Image quality assessment for determining efficacy and limitations of Super-Resolution Convolutional Neural Network (SRCNN)—0
Medical image super-resolution method based on dense blended attention network—0
Ensemble Super-Resolution with A Reference DatasetCode0
A Cone-Beam X-Ray CT Data Collection designed for Machine LearningCode0
Handheld Multi-Frame Super-ResolutionCode0
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified