Noise reduction in a SAR image using an updating strategy
Encadrants : Florence Tupin, Emanuele Dalsasso
Email : email@example.com
Disponible : OUI
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 use a set of previously acquired images to help suppressing the noise in a newly acquired data. A first strategy will be to compute the mean of the available dates to create a super-image and train a network to reduce the noise. In a second step, the interest of updating (the "super-image" corresponding to the previously denoised date) willbe investigated. In this situation, the level of change between the two dates should be low and may improve the noise reduction step. Experiments will first be done in a supervised framework using the network artchitecture proposed in .
Liens complémentaires : References  Dalsasso, E., Yang, X., Denis, L., Tupin, F., & Yang, W. (2020). SAR image despeckling by deep neural networks: From a pre-trained model to an end-to-end training strategy. Remote Sensing, 12(16), 2636. https://www.mdpi.com/2072-4292/12/16/2636/pdf  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