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Created by sebastien.popoff on 26/01/2021
Maximum information states for coherent scattering measurements
[D. Bouchet et al., Nat. Phys., 71 (2021)] Coherent light is a popular tool for sensing and imaging. In simple cases, one can guess or compute a spatial and/or temporal excitation beam profile that ensures that the information about a specific target can be retrieved. However, there was no general rule to find the optimal states of light that maximize the precision of a given parameter estimation. Moreover, such states are expected to depend heavily on the parameter one tries to measure. In the present paper, the authors define a general framework to identify such optimal spatial channels, regardless of the complexity of the propagating medium, using the measurement of the transmission matrix. They demonstrate this concept using wavefront shaping to probe the phase and the position of a target hidden behind a static scattering medium. |
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Created by sebastien.popoff on 14/12/2020
Time reversed optical waves by arbitrary vector spatiotemporal field generation
[M. Mounaix et al., Nat. Commun., 11 (2020)] Time-reversal allows precisely tailoring the spatio-temporal field and was originally demonstrated in acoustics. Time-reversal requires to temporally modulate the optical field independently over a large number of pixels, which is challenging in optical experiments. In the present paper, the authors developed a system allowing the modulation of the optical field spectrally and spatially over a 2d array. Harnessing this new tool, they perform a time-reversal experiment to focus and shape the optical field temporally and spatially through a multimode fiber. |
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Created by admin on 13/11/2020
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. |
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Created by sebastien.popoff on 02/11/2020
Learning and avoiding disorder in multimode fibers
[M.W. Matthès, arxiv, 2010.14813 (2020)] In the past 10+ years, numerous advances were made for endoscopic imaging, micromanipulation, or telecommunication applications with multimode fiber. The main limitation to real-life applications is the sensitivity to perturbations that sometimes causes the transmission property of the fiber to change in real-time. To address this issue, the authors (we) show that, even in the presence of strong perturbations, there exists a set of channels that are almost insensitive to perturbations. Interestingly, these channels can be found using only measurements from small perturbation leveraging the so-called generalized Wigner-Smith operator. This requires the measurement of the transmission matrix, which is done thanks to a new technique based on deep learning frameworks that compensate automatically for misalignments and aberrations, allowing fast and easy acquisitions. |
