Created by admin on 13/11/2020

Highlights

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 image 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 06/10/2020

Highlights

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.

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Created by sebastien.popoff on 25/08/2020

Highlights

Using prior information for speeding up the measurement of fiber transmission matrices

[S. Li et al., arxiv, 2007.15891, (2020)]

Due to disorder and dispersion, knowing the transmission matrix of a multimode fiber is usually required to reconstruct an input image for endoscopic applications. In the general case, its characterization for a fiber allowing \(N\) guided modes requires at least \(N\) complex measurements. However, we usually have additional information, the most common one being that the matrix is never totally random, and usually sparse, when expressed in the mode basis. In this study, the authors use such prior information to reduce drastically the number of measurements for the transmission matrix estimation using the framework of compressed sensing. They demonstrate the validity of such an approach for endoscopic imaging through multimode fibers.

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Created by sebastien.popoff on 03/08/2020

Highlights

Parallelized STED microscopy using tailored speckles

[N. Bender et al., arxiv, 2007.15491 (2020)]

Super-resolution fluorescence microscopy techniques, such as stimulated emission depletion (STED), rely on depleting fluorescence around a region smaller than the limit of diffraction. This can be achieved with a doughnut-shaped beam that is then scanned to produce an image. Such a process is time-consuming. Structured illumination techniques were proposed to parallelize the process by having multiple zeros of the field in the same image, for example with an array of doughnut beams. However, it typically limits optical sectioning as the field conserves its shape for quite large distances along the axial direction. One way to overcome this limitation is to use speckle patterns. Speckle exhibits numerous singularities, allowing parallelization of the technique, and they rapidly and non-repeatably change along the axial direction, guarantying the optical sectioning while being robust to aberrations. The issue is that speckle singularities (optical vortices) are not isotropic, leading to distortions of the image. In the present paper, N. Bender and co-authors use wavefront shaping to design ideal speckle patterns for non-linear microscopy to achieve isotropic and uniform super-resolution.

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