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 3101–3110 of 3874 papers

TitleStatusHype
An Application of Generative Adversarial Networks for Super Resolution Medical Imaging—0
Scale-wise Convolution for Image RestorationCode1
Lightweight and Robust Representation of Economic Scales from Satellite ImageryCode0
AeroRIT: A New Scene for Hyperspectral Image AnalysisCode1
Adaptive Densely Connected Super-Resolution ReconstructionCode0
Spatial-Angular Interaction for Light Field Image Super-ResolutionCode1
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
Inferring Super-Resolution Depth from a Moving Light-Source Enhanced RGB-D Sensor: A Variational ApproachCode0
An Approach to Super-Resolution of Sentinel-2 Images Based on Generative Adversarial Networks—0
Memory-efficient Learning for Large-scale Computational Imaging -- NeurIPS deep inverse workshop—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified