Publications de François Thierry M Marelli

Efficient compressed sensing reconstruction for 3D fluorescence microscopy using OptoMechanical Modulation Tomography (OMMT) with a 1+2D regularization

OptoMechanical Modulation Tomography (OMMT) exploits compressed sensing to reconstruct high resolution microscopy volumes from fewer measurement images compared to exhaustive section sampling in conventional light sheet microscopy. Nevertheless, the volume ...

2023

Practical computational imaging by use of spatiotemporal light modulation: from simulations to applications in biological microscopy

François Thierry M Marelli

Optical microscopy is an essential tool for biologists, who are often faced with the need to overcome the spatial and temporal resolution limitations of their devices to capture finer details. As upgrading imaging hardware is expensive, computational metho ...

EPFL2023

Mechanical artifacts in optical projection tomography: classification and automatic calibration

Michaël Unser, Thanh-An Michel Pham, Jonathan Yuelin Dong, François Thierry M Marelli

Optical projection tomography (OPT) is a powerful tool for biomedical studies. It achieves 3D visualization of mesoscopic biological samples with high spatial resolution using conventional tomographic-reconstruction algorithms. However, various artifacts d ...

Optica Publishing Group2022

Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks

Michael Stefan Daniel Liebling, François Thierry M Marelli

Many applications rely on thermal imagers to complement or replace visible light sensors in difficult imaging conditions. Recent advances in machine learning have opened the possibility of analyzing or enhancing images, yet these methods require large anno ...

SPIE-INT SOC OPTICAL ENGINEERING2021

An End-to-End Networks to Synthetize Intonation using a Generalized Command Response Model

Hervé Bourlard, Philip Neil Garner, Bastian Schnell, François Thierry M Marelli

The generalized command response (GCR) model represents intonation as a superposition of muscle responses to spike command signals. We have previously shown that the spikes can be predicted by a two-stage system, consisting of a recurrent neural network an ...

Idiap2019