Publications de Navid Borhani | EPFL Graph Search

Multicore fiber endoscope for phase imaging based on intensity recording using deep neural networks

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava

The present invention is related to an endoscopic system for phase imaging, comprising a multicore waveguide (080), an optical system comprising at least one first light source (002), for illuminating the sample to be examined, a first camera (010) that is ...

2022

Full characterization of partially measured systems with neural networks

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

IEEE2021

Learning to See and Compute through Multimode Fibers

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

We propose a computational method for controlling the output of a multimode fiber using machine learning. Arbitrary images can be projected with amplitude-only calibration (no phase measurement) and fidelities on par with conventional full-measurement meth ...

IEEE2021

Deep Learning-Based Image Classification through a Multimode Fiber in the Presence of Wavelength Drift

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin

Deep neural networks (DNNs) are employed to recover information after its propagation through a multimode fiber (MMF) in the presence of wavelength drift. The intensity distribution of the speckle patterns generated at the output of an MMF when an input wa ...

2020

Controlling spatiotemporal nonlinearities in multimode fibers with deep neural networks

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava, Babak Rahmani, Ugur Tegin

Spatiotemporal nonlinear interactions in multimode fibers are of interest for beam shaping and frequency conversion by exploiting the nonlinear interaction of different pump modes from quasi-continuous wave to ultrashort pulses centered around visible to i ...

2020

Actor neural networks for the robust control of partially measured nonlinear systems showcased for image propagation through diffuse media

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Ugur Tegin

The output of physical systems, such as the scrambled pattern formed by shining the spot of a laser pointer through fog, is often easily accessible by direct measurements. However, selection of the input of such a system to obtain a desired output is diffi ...

SPRINGERNATURE2020

Imaging through multimode fibers using deep learning: The effects of intensity versus holographic recording of the speckle pattern

Demetri Psaltis, Christophe Moser, Navid Borhani, Damien Claude-Marie Loterie, Eirini Kakkava, Babak Rahmani, Georgia Konstantinou, Ugur Tegin

Information transmission through multimode fibers (MMFs) has been a topic of great interest for many years. Deep learning algorithms have been applied successfully to MMFs in particular to fiber endoscopy. In this work, we show how Deep Neural Networks (DN ...

2019

Digital staining through the application of deep neural networks to multi-modal multi-photon microscopy

Demetri Psaltis, Navid Borhani

Deep neural networks have been used to map multi-modal, multi-photon microscopy measurements of a label-free tissue sample to its corresponding histologically stained brightfield microscope colour image. It is shown that the extra structural and functional ...

2019

Deep Neural Networks for seeing through multimode fibers

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava

Image delivery through multimode fibers (MMFs) suffers from modal scrambling which results in a speckle pattern at the fiber output. In this work, we use Deep Neural Networks (DNNs) for recovery and/or classification of the input image from the intensity-o ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Learning to see through multimode fibers

Demetri Psaltis, Christophe Moser, Navid Borhani, Eirini Kakkava

2018