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 961–970 of 3874 papers

TitleStatusHype
Super-Resolution-based Snake Model -- An Unsupervised Method for Large-Scale Building Extraction using Airborne LiDAR Data and Optical ImageCode1
DeepBedMap: Using a deep neural network to better resolve the bed topography of AntarcticaCode1
MXR-U-Nets for Real Time Hyperspectral ReconstructionCode1
4DFlowNet: Super-Resolution 4D Flow MRI using Deep Learning and Computational Fluid DynamicsCode1
KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflowCode1
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-ResolutionCode1
Deep Adaptive Inference Networks for Single Image Super-ResolutionCode1
Learning A Single Network for Scale-Arbitrary Super-ResolutionCode1
Multimodal Image Synthesis with Conditional Implicit Maximum Likelihood EstimationCode1
Deformable 3D Convolution for Video Super-ResolutionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified