Search Results for author: Mauro Dalla Mura

Found 11 papers, 6 papers with code

Koopman Ensembles for Probabilistic Time Series Forecasting

1 code implementation11 Mar 2024 Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Albdeldjalil Aïssa El Bey

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed.

Probabilistic Time Series Forecasting Time Series +1

MultiHU-TD: Multifeature Hyperspectral Unmixing Based on Tensor Decomposition

2 code implementations5 Oct 2023 Mohamad Jouni, Mauro Dalla Mura, Lucas Drumetz, Pierre Comon

Matrix models become insufficient when the hyperspectral image (HSI) is represented as a high-order tensor with additional features in a multimodal, multifeature framework.

Hyperspectral Unmixing Tensor Decomposition

Neural Koopman prior for data assimilation

1 code implementation11 Sep 2023 Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aïssa El Bey

With the increasing availability of large scale datasets, computational power and tools like automatic differentiation and expressive neural network architectures, sequential data are now often treated in a data-driven way, with a dynamical model trained from the observation data.

Self-Supervised Learning Time Series

Model-based demosaicking for acquisitions by a RGBW color filter array

no code implementations2 Jun 2023 Matthieu Muller, Daniele Picone, Mauro Dalla Mura, Magnus O Ulfarsson

Microsatellites and drones are often equipped with digital cameras whose sensing system is based on color filter arrays (CFAs), which define a pattern of color filter overlaid over the focal plane.


Learning Sentinel-2 reflectance dynamics for data-driven assimilation and forecasting

1 code implementation5 May 2023 Anthony Frion, Lucas Drumetz, Guillaume Tochon, Mauro Dalla Mura, Abdeldjalil Aïssa El Bey

Over the last few years, massive amounts of satellite multispectral and hyperspectral images covering the Earth's surface have been made publicly available for scientific purpose, for example through the European Copernicus project.

Self-Supervised Learning Time Series

Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data

no code implementations13 Mar 2023 Anthony Frion, Lucas Drumetz, Mauro Dalla Mura, Guillaume Tochon, Abdeldjalil Aissa El Bey

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics.

Joint demosaicing and fusion of multiresolution coded acquisitions: A unified image formation and reconstruction method

1 code implementation3 Sep 2022 Daniele Picone, Mauro Dalla Mura, Laurent Condat

Novel optical imaging devices allow for hybrid acquisition modalities such as compressed acquisitions with locally different spatial and spectral resolutions captured by a single focal plane array.

Demosaicking Image Reconstruction

An Introduction to Deep Morphological Networks

no code implementations4 Jun 2019 Keiller Nogueira, Jocelyn Chanussot, Mauro Dalla Mura, Jefersson A. dos Santos

Results show that the proposed DeepMorphNets is a promising technique that can learn distinct features when compared to the ones learned by current deep learning methods.

Image Classification

Dynamic Multi-Context Segmentation of Remote Sensing Images based on Convolutional Networks

1 code implementation11 Apr 2018 Keiller Nogueira, Mauro Dalla Mura, Jocelyn Chanussot, William R. Schwartz, Jefersson A. dos Santos

A systematic evaluation of the proposed algorithm is conducted using four high-resolution remote sensing datasets with very distinct properties.

Semantic Segmentation

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