Search Results for author:
Found 8 papers, 2 papers with code
Atmospheric Turbulence Removal with Video Sequence Deep Visual Priors
Atmospheric turbulence poses a challenge for the interpretation and visual perception of visual imagery due to its distortion effects.
Contextual colorization and denoising for low-light ultra high resolution sequences
Experimental results show that our method outperforms existing approaches in terms of subjective quality and that it is robust to variations in brightness levels and noise.
Atmospheric turbulence removal using convolutional neural network
This paper describes a novel deep learning-based method for mitigating the effects of atmospheric distortion.
The application of Convolutional Neural Networks to Detect Slow, Sustained Deformation in InSAR Timeseries
Automated systems for detecting deformation in satellite InSAR imagery could be used to develop a global monitoring system for volcanic and urban environments.
DefectNET: multi-class fault detection on highly-imbalanced datasets
As a data-driven method, the performance of deep convolutional neural networks (CNN) relies heavily on training data.
Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion
This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects.
Detecting Volcano Deformation in InSAR using Deep learning
Globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active, but half of these have no ground-based monitoring.
Automatic Leaf Extraction from Outdoor Images
Automatic plant recognition and disease analysis may be streamlined by an image of a complete, isolated leaf as an initial input.
