Harnessing disorder and symmetries in multimode fibers

Sébastien M. Popoff
IESC Cargèse, France
April 2024

We present how to study light propagation in multimode fibers using transmission matrix measurements and apply it to understand the effect disorder and its impact on the so-called rotational memory effect. This talk is part of the Wave Propagation and Control in Complex Media 2024 workshop (15-19 April 2024, Cargèse, France)

Dynamic structured illumination for confocal microscopy

Structured illumination enhances the resolution of a standard microscope by encoding the high spatial frequencies of an object's image into lower spatial frequencies through the use of a carefully selected pattern. In essence, it modifies the optical transfer function (OTF), which is the Fourier Transform of the point spread function (PSF), to increase sensitivity to high spatial frequencies. In [G. Noetinger et al, Arxiv 2306.14631 (2023)], the authors introduce a novel technique that further leverages time by incorporating a temporal periodic modulation, specifically through the use of a rotating mask, to encode multiple transfer functions within the temporal domain. This methodology is exemplified using a confocal microscope setup. At each scanning position, a temporal periodic signal is captured, enabling the construction of multiple images of the same object. The image carried by each harmonic is a convolution of the object with a phase vortex of topological charge, similar to the outcome when using a vortex phase plate as an illumination. This enables the collection of chosen high spatial frequencies from the sample, thereby enhancing the spatial resolution of the confocal microscope.

Image reconstruction through unknown random configurations of multimode fibers using deep learning

[S. Resisi et al, arxiv, 2011.05144 (2020)]

Multimode fibers tend to scramble input images due to intermodal dispersion and random mode coupling. While this scrambling effect can be learned, for instance by measuring the transmission matrix of the fiber, slight changes of the geometrical conformation of the fiber modify its response, making the calibration obsolete. In the present paper, the authors use deep-learning using data sets acquired over a wide range of deformations to reconstruct images sent through unknown configurations of multimode fibers. This is possible thanks to the presence of invariant properties that the numerical model learns.

Model-based wavefront shaping microscopy

[A. Thendiyammal et al.,  Opt. Lett., 45 (2020)]

Wavefront shaping offers the possibility to increasing microscopic imaging depth. By learning how to focus deep inside a (not too) scattering medium, we also learn how to compensate for scattering effects around this area, allowing us to retrieve an image of this area. Typically, finding the wavefront that focuses light at a given target is done using a feedback optimization procedure, or by measuring the response of the system. In this paper, the authors propose another approach. They first create a model of the system thanks to some calibration measurements. The model is then used for finding the optical input wavefront that would be utilized for imaging at different depths. They experimentally demonstrate the advantage of this technique for two-photon fluorescent imaging through a low scattering medium.