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 3591–3600 of 3874 papers

TitleStatusHype
Super-resolution of spatiotemporal event-stream image captured by the asynchronous temporal contrast vision sensor—0
Multispectral Compressive Imaging Strategies using Fabry-Pérot Filtered Sensors—0
Orthogonally Regularized Deep Networks For Image Super-resolution—0
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation NetworksCode0
tempoGAN: A Temporally Coherent, Volumetric GAN for Super-resolution Fluid FlowCode0
Accurate Weakly Supervised Deep Lesion Segmentation on CT Scans: Self-Paced 3D Mask Generation from RECIST—0
Denoising Prior Driven Deep Neural Network for Image RestorationCode0
Frame-Recurrent Video Super-Resolution—0
How should a fixed budget of dwell time be spent in scanning electron microscopy to optimize image quality?—0
Light Field Super-Resolution using a Low-Rank Prior and Deep Convolutional Neural Networks—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified