Multi-temporal SAR denoising : Multiple Input Multiple Output strategy

Encadrants : Florence Tupin, Emanuele Dalsasso
Disponible : NON
Spécialité : IMA
Nombre d'étudiants : 1
Description :

SAR (Synthetic Aperture Radar) images have the advantage of being
acquired at any time (day or night and whatever the weather conditions).
However, they are very noisy because of the speckle noise due to
coherent imaging. The acquisition of several images and their
combination allows to reduce very efficiently the noise present on the
data.

Travail demandé : The aim of this project is to train a deep neural network to
simultaneously denoise multiple images in a MIMO framework (Multiple
Input Multiple Output). The training will be done in a self-supervised
way using the paradigm and architecture proposed in [1]. A comparison
with the Multiple Input Single Output framework developed in [2] will be
done.
Liens complémentaires : References
[1] Dalsasso, Emanuele, Loïc Denis, and Florence Tupin. "As if by magic:
self-supervised training of deep despeckling networks with MERLIN." IEEE
Transactions on Geoscience and Remote Sensing 60 (2021): 1-13.
https://arxiv.org/abs/2110.13148

[2] Multi-temporal speckle reduction with self-supervised deep neural
networks
Inès Meraoumia, Emanuele Dalsasso, Loïc Denis, Rémy Abergel, Florence Tupin
https://arxiv.org/pdf/2207.11095