177 citations · 551 across the 17 of their papers we have counts for
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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…
Learning Graph-Convolutional Representations for Point Cloud Denoising
Francesca Pistilli, Giulia Fracastoro, Diego Valsesia +1
Point clouds are an increasingly relevant data type but they are often corrupted by noise. We propose a deep neural network based on graph-convolutional layers that can elegantly d…
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…