Compensating for phase drifts in holographic measurements

Digital holography allows measuring the complex amplitude of a given wavefront. We presented in detail the off-axis holography approach. However, it requires a separate reference arm. Due to air flow, vibrations, or other perturbations, the optical path length difference between the two arms can fluctuate in time, even in controlled lab experiments on a good optical table. This means that the phase of the measured wavefront is estimated up to a global phase that can randomly change over time. This is very detrimental for transmission matrix measurements as the relative phase between each column has to be precisely estimated. This is particularly true when the measurement time can take few minutes or more when using a liquid crystal spatial light modulator that has a limited frame rate. In [R. Mouthaan et al., Appl. Opt. (2022)], the authors propose a simple yet robust way to compensate for phase fluctuations, even when the phase changes completely between two frames.

Setting up a DMD/SLM: Aberration effects

Digital Micromirror Devices (DMDs) are amplitude only (binary) modulators, however, pretty much like liquid crystal modulators, they introduce some phase distortion. Practically, it means that if one illuminates the modulator with a plane wave, even when all the pixels are set to the same value, the wavefront shows phase distortions after reflection. That can be detrimental, especially when working in a plane conjugated with the Fourier plane of the DMD surface. Fortunately, using the Lee hologram method or the superpixel method, one can achieve phase modulation. I present here how to use Lee holograms to characterize and compensate for aberrations when using a DMD. This approach can also be applied for compensating for aberration effects in other types of Spatial Light Modulators, such as liquid crystal ones.

Compare Different Methods of Modes Estimation of Bent Multimode Fibers with pyMMF

In a previous tutorial, I explained how to calculate the modes of a bent multimode fibers. I introduced two methods, following the approach published in [M. Plöschner, T. Tyc, and T. Čižmár, Nat. Photon. (2015)]. In this short tutorial, I show how to use pyMMF to simulate bent fibers and compare the two different methods. A Jupyter notebook can be found on my Github account: compare_bending_methods.ipynb.

Semidefinite Programming for Intensity Only Estimation of the Transmission Matrix

The possibility of measuring the transmission matrix using intensity only measurements is a much sought-after feature as it allows us not to rely on interferometry. Interferometry usually requires laboratory-grade stability difficult to obtain for real-world applications. Typically, we want to be able to retrieve the transmission matrix from a set of pairs composed of input masks and output intensity patterns. However, this problem, which corresponds to a phase-retrieval problem, is not convex, hence difficult to solve using standard techniques. The idea proposed in [I. Waldspurger et al., Math. Program (2015)] is to relax some constraints to approximate the problem to a convex one that can be solved using the semidefinite programming approach. I briefly detail the approach and provide an example of the procedure to reconstruct the transmission matrix using Python. A Jupyter notebook can be found on my Github account: semidefiniteTM_example.ipynb.