How to Correct for Bias in Off-Axis Holography for Transmission Matrix Measurements

Sébastien M Popoff¹ , Antoine Loquet¹

I presented in a previous tutorial how to reconstruct a complex field using a camera and a plane wave reference tilted with respect to the optical axis, known as off-axis holography. This works perfectly with an ideal plane wave as a reference. Of course, real life is not perfect, and the reference usually presents imperfections. While low spatial fluctuations can be compensated for afterward, high spatial frequency noise has the effect of adding a small bias to the estimation of the field. Such bias is typically small, but in transmission matrix measurements—since it is static and added to all measurements—it can affect the singular value distribution and perturb or hide transmission channels that would otherwise be visible.

ZoomFFT for speeding up off-axis computation (and more)

Sébastien M Popoff¹  and Rodrigo Gutiérrez-Cuevas¹

When performing the computation for some tasks such as off-axis holography, we often have to compute the entire FFT of a signal or an image while being only interested in a very small part of the spectrum. The rest of the information is just discarded. While the FFT algorithm and its implementation in standard computing libraries are very efficient, we can still take advantage of a slower approach which only computes what we need. The ZoomFFT algorithm does just that! And it's already available in standard packages such as SciPy for Python or in MATLAB. Using off-axis holography as an example, I will show how to save time – or not – using ZoomFFT.

Master intership + PhD at the Langevin Institute

Invariant Properties in Multimode Fibers for Imaging Applications

We are recruiting a master student with the possibility to continue during a Ph.D (funded) to work on the study of light propagation in multimode fibers using wavefront shaping and numerical reconstruction algorithms (phase retrieval, deep learning). Join un in Paris!

Keywords: waveftont shaping, mutlimode fibers, mesoscopic physics, phase retrieval, deep learning

See our recent publication: 

• [Learning and avoiding disorder in multimode fibers, Phys. Rev. X, 2021]
• [Tailoring the Rotational Memory Effect in Multimode Fibers, arxiv, 2023]

TL;DR:
We will play with deep learning frameworks to develop new approaches for calibration-less imaging through multimode fibers based on the study of invariant properties in multimode fibers.

Contact: Sébastien Popoff - sebastien.popoff(at)espci.fr

More information here.