177 citations · 551 across the 17 of their papers we have counts for
8 papers · 1 filter
Semi-supervised learning for joint SAR and multispectral land cover classification
Antonio Montanaro, Diego Valsesia, Giulia Fracastoro +1
Semi-supervised learning techniques are gaining popularity due to their capability of building models that are effective, even when scarce amounts of labeled data are available. In…
Deep Learning Methods For Synthetic Aperture Radar Image Despeckling: An Overview Of Trends And Perspectives
Giulia Fracastoro, Enrico Magli, Giovanni Poggi +3
Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Desp…
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Information extraction from synthetic aperture radar (SAR) images is heavily impaired by speckle noise, hence despeckling is a crucial preliminary step in scene analysis algorithms…
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Deep learning methods for super-resolution of a remote sensing scene from multiple unregistered low-resolution images have recently gained attention thanks to a challenge proposed…
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
SAR despeckling is a problem of paramount importance in remote sensing, since it represents the first step of many scene analysis algorithms. Recently, deep learning techniques hav…
Deep Graph-Convolutional Image Denoising
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Non-local self-similarity is well-known to be an effective prior for the image denoising problem. However, little work has been done to incorporate it in convolutional neural netwo…
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Andrea Bordone Molini, Diego Valsesia, Giulia Fracastoro +1
Recently, convolutional neural networks (CNN) have been successfully applied to many remote sensing problems. However, deep learning techniques for multi-image super-resolution fro…
Image Denoising with Graph-Convolutional Neural Networks
Diego Valsesia, Giulia Fracastoro, Enrico Magli
Recovering an image from a noisy observation is a key problem in signal processing. Recently, it has been shown that data-driven approaches employing convolutional neural networks…