arXiv subjects

Claire Lefort

Publications and source records attributed to Claire Lefort.

3 recordsLinked to original sources

arXiv2026

Optical Poling Reveals Hidden Molecular Restructuring in Multimode Fibers, Unlocking Ultra-Efficient Third-Order Nonlinearities

Optical poling is a well-established technique for inducing \chi^{(2)} nonlinearity, yet its impact on silica's molecular structure remains unexplored. Here, we report the first direct observation of molecular restructuring in large-core graded-index multimode fibers (MMFs) induced by optical poling, transforming the silica tetrahedral ring network. Through coherent light beating, this process converts large rings of more than four SiO_4 tetrahedra into smaller ones, altering both linear and nonlinear optical susceptibilities. Contrary to the assumption that poling efficiency stems solely from charge displacement, we show that structural modifications dominate, leading to record enhancements in third-order nonlinear processes, including geometric parametric instabilities (GPIs) and Kerr self-cleaning, despite a low modification of the Kerr coefficient. High-energy poling acts as an in situ annealing process, dynamically modulating the refractive index for unprecedented spatiotemporal light control. These findings provide fundamental insights into silica's molecular dynamics under intense optical fields and open avenues for ultra-efficient nonlinear optical devices, enabling next-generation fiber-based photonics for high-power lasers, broadband light generation, and all-optical signal processing.

physics.optics↗

arXiv2024

Exploitation of the nonresonant background of Multiplex-Coherent anti-Stokes Raman Scattering for label-free discrimination of proteins

We propose a novel approach using Multiplex-Coherent Anti-Stokes Raman Scattering (M-CARS) for la-bel-free discriminations in biomedical tissues. The strategy is based on the evaluation of the contrast be-tween resonant and nonresonant contributions in a M-CARS hyperspectral dataset, and tested to identify and differentiate thin actin filaments from thick myosin filaments in muscle tissue without any labeling. First step consists in ensuring knowledge of the spatial regions containing thick myosin filaments thanks to its endogenous second harmonic signal, deducing expected location for thin actin filaments between myosin filaments. The ratio of resonant and nonresonant contributions for each pixel of the hyperspectral image allows then to discriminate actin from myosin filaments, whose localization is in accordance with the SHG probing. This qualitative imaging represents a proof of principle for highlighting and discriminat-ing purposes in biological microscopy, thanks to the difference in the nonlinear properties of the related proteins. This paves the way for considering label-free imaging through a competition between two third-order nonlinear signatures.

physics.med-ph↗

arXiv2023

A Novel Variational Approach for Multiphoton Microscopy Image Restoration: from PSF Estimation to 3D Deconvolution

In multi-photon microscopy (MPM), a recent in-vivo fluorescence microscopy system, the task of image restoration can be decomposed into two interlinked inverse problems: firstly, the characterization of the Point Spread Function (PSF) and subsequently, the deconvolution (i.e., deblurring) to remove the PSF effect, and reduce noise. The acquired MPM image quality is critically affected by PSF blurring and intense noise. The PSF in MPM is highly spread in 3D and is not well characterized, presenting high variability with respect to the observed objects. This makes the restoration of MPM images challenging. Common PSF estimation methods in fluorescence microscopy, including MPM, involve capturing images of sub-resolution beads, followed by quantifying the resulting ellipsoidal 3D spot. In this work, we revisit this approach, coping with its inherent limitations in terms of accuracy and practicality. We estimate the PSF from the observation of relatively large beads (approximately 1$\mu$m in diameter). This goes through the formulation and resolution of an original non-convex minimization problem, for which we propose a proximal alternating method along with convergence guarantees. Following the PSF estimation step, we then introduce an innovative strategy to deal with the high level multiplicative noise degrading the acquisitions. We rely on a heteroscedastic noise model for which we estimate the parameters. We then solve a constrained optimization problem to restore the image, accounting for the estimated PSF and noise, while allowing a minimal hyper-parameter tuning. Theoretical guarantees are given for the restoration algorithm. These algorithmic contributions lead to an end-to-end pipeline for 3D image restoration in MPM, that we share as a publicly available Python software. We demonstrate its effectiveness through several experiments on both simulated and real data.

eess.IV↗