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 3291–3300 of 3874 papers

TitleStatusHype
3D Appearance Super-Resolution with Deep LearningCode0
Super-resolution of Time-series Labels for Bootstrapped Event Detection—0
Residual Networks for Light Field Image Super-Resolution—0
Hyperspectral Image Super-Resolution With Optimized RGB GuidanceCode0
Second-Order Attention Network for Single Image Super-ResolutionCode0
Zoom to Learn, Learn to Zoom—0
ODE-Inspired Network Design for Single Image Super-Resolution—0
Towards Real Scene Super-Resolution with Raw ImagesCode1
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
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified