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 1021–1030 of 3874 papers

TitleStatusHype
Underwater Image Super-Resolution using Deep Residual MultipliersCode1
TextSR: Content-Aware Text Super-Resolution Guided by RecognitionCode1
Learning Filter Basis for Convolutional Neural Network CompressionCode1
Learned Image Downscaling for Upscaling using Content Adaptive ResamplerCode1
Single Image Super-Resolution via CNN Architectures and TV-TV MinimizationCode1
Towards Real Scene Super-Resolution with Raw ImagesCode1
Zoom To Learn, Learn To ZoomCode1
EDVR: Video Restoration with Enhanced Deformable Convolutional NetworksCode1
SinGAN: Learning a Generative Model from a Single Natural ImageCode1
Guided Super-Resolution as Pixel-to-Pixel TransformationCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified