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01/19/2023
Fast Inference in Denoising Diffusion Models via MMD Finetuning
Denoising Diffusion Models (DDMs) have become a popular tool for generat...
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09/21/2022
Rethinking the compositionality of point clouds through regularization in the hyperbolic space
Point clouds of 3D objects exhibit an inherent compositional nature wher...
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09/20/2022
Cross-modal Learning for Image-Guided Point Cloud Shape Completion
In this paper we explore the recent topic of point cloud completion, gui...
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07/01/2022
Exploring the solution space of linear inverse problems with GAN latent geometry
Inverse problems consist in reconstructing signals from incomplete sets ...
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04/06/2022
Super-resolved multi-temporal segmentation with deep permutation-invariant networks
Multi-image super-resolution from multi-temporal satellite acquisitions ...
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08/20/2021
Self-supervised learning for joint SAR and multispectral land cover classification
Self-supervised learning techniques are gaining popularity due to their ...
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05/26/2021
Permutation invariance and uncertainty in multitemporal image super-resolution
Recent advances have shown how deep neural networks can be extremely eff...
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03/30/2021
Denoise and Contrast for Category Agnostic Shape Completion
In this paper, we present a deep learning model that exploits the power ...
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03/29/2021
RAN-GNNs: breaking the capacity limits of graph neural networks
Graph neural networks have become a staple in problems addressing learni...
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12/10/2020
Deep learning methods for SAR image despeckling: trends and perspectives
Synthetic aperture radar (SAR) images are affected by a spatially-correl...
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07/06/2020
Learning Graph-Convolutional Representations for Point Cloud Denoising
Point clouds are an increasingly relevant data type but they are often c...
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07/04/2020
Speckle2Void: Deep Self-Supervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
Information extraction from synthetic aperture radar (SAR) images is hea...
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01/15/2020
Towards Deep Unsupervised SAR Despeckling with Blind-Spot Convolutional Neural Networks
SAR despeckling is a problem of paramount importance in remote sensing, ...
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01/15/2020
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene fro...
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09/02/2019
Analysis of SparseHash: an efficient embedding of set-similarity via sparse projections
Embeddings provide compact representations of signals in order to perfor...
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07/19/2019
Deep Graph-Convolutional Image Denoising
Non-local self-similarity is well-known to be an effective prior for the...
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07/15/2019
DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images
Recently, convolutional neural networks (CNN) have been successfully app...
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07/05/2019
High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
Compression of hyperspectral images onboard of spacecrafts is a tradeoff...
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05/29/2019
Image Denoising with Graph-Convolutional Neural Networks
Recovering an image from a noisy observation is a key problem in signal ...
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04/17/2018
Sampling of graph signals via randomized local aggregations
Sampling of signals defined over the nodes of a graph is one of the cruc...
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10/08/2013
Smoothness-Constrained Image Recovery from Block-Based Random Projections
In this paper we address the problem of visual quality of images reconst...
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10/04/2013