7 research outputs found

    Computational optical imaging with a photonic lantern

    Get PDF
    [EN] The thin and flexible nature of optical fibres often makes them the ideal technology to view biological processes in-vivo, but current microendoscopic approaches are limited in spatial resolution. Here, we demonstrate a route to high resolution microendoscopy using a multicore fibre (MCF) with an adiabatic multimode-to-single-mode "photonic lantern" transition formed at the distal end by tapering. We show that distinct multimode patterns of light can be projected from the output of the lantern by individually exciting the single-mode MCF cores, and that these patterns are highly stable to fibre movement. This capability is then exploited to demonstrate a form of single-pixel imaging, where a single pixel detector is used to detect the fraction of light transmitted through the object for each multimode pattern. A custom computational imaging algorithm we call SARA-COIL is used to reconstruct the object using only the pre-measured multimode patterns themselves and the detector signals.This work was funded through the "Proteus" Engineering and Physical Sciences Research Council (EPSRC) Interdisciplinary Research Collaboration (IRC) (EP/K03197X/1), by the Science and Technology Facilities Council (STFC) through STFC-CLASP grants ST/K006509/1 and ST/K006460/1, STFC Consortium grants ST/N000625/1 and ST/N000544/1. S.L. acknowledges support from the National Natural Science Foundation of China under Grant no. 61705073. DBP acknowledges support from the Royal Academy of Engineering, and the European Research Council (PhotUntangle, 804626). The authors thank Philip Emanuel for the use of his confocal image of A549 cells and Eckhardt Optics for their image of the USAF 1951 target. The authors sincerely thank the anonymous reviewers of this paper for their detailed and considered feedback which helped us to improve the quality of this paper significantly.Choudhury, D.; Mcnicholl, DK.; Repetti, A.; Gris-Sánchez, I.; Li, S.; Phillips, DB.; Whyte, G.... (2020). Computational optical imaging with a photonic lantern. Nature Communications. 11(1):1-9. https://doi.org/10.1038/s41467-020-18818-6S19111Wood, H. A. C., Harrington, K., Birks, T. A., Knight, J. C. & Stone, J. M. High-resolution air-clad imaging fibers. Opt. Lett. 43, 5311–5314 (2018).Akram, A. R. et al. In situ identification of Gram-negative bacteria in human lungs using a topical fluorescent peptide targeting lipid A. Sci. Transl. Med. 10, eaal0033 (2018).Shin, J., Bosworth, B. T. & Foster, M. A. Compressive fluorescence imaging using a multi-core fiber and spatially dependent scattering. Opt. Lett. 42, 109–112 (2017).Papadopoulos, I. N., Farahi, S., Moser, C. & Psaltis, D. Focusing and scanning light through a multimode optical fiber using digital phase conjugation. Opt. Express 20, 10583–10590 (2012).Čižmár, T. & Dholakia, K. Exploiting multimode waveguides for pure fibre-based imaging. Nat. Commun. 3, 1027 (2012).Plöschner, M., Tyc, T. & Čižmár, T. Seeing through chaos in multimode fibres. Nat. Photon. 9, 529–535 (2015).Birks, T. A., Gris-Sánchez, I., Yerolatsitis, S., Leon-Saval, S. G. & Thomson, R. R. The photonic lantern. Adv. Opt. Photon. 7, 107–167 (2015).Birks, T. A., Mangan, B. J., Díez, A., Cruz, J. L. & Murphy, D. F. Photonic lantern’ spectral filters in multi-core fiber. Opt. Express 20, 13996–14008 (2012).Edgar, M. P., Gibson, G. M. & Padgett, M. J. Principles and prospects for single-pixel imaging. Nat. Photon. 13, 13–20 (2019).Mahalati, R. N., Yu, Gu. R. & Kahn, J. M. Resolution limits for imaging through multi-mode fiber. Opt. Express 21, 1656–1668 (2013).Amitonova, L. V. & de Boer, J. F. Compressive imaging through a multimode fiber. Opt. Lett. 43, 5427–5430 (2018).Mallat, S. A Wavelet Tour of Signal Processing 2nd edn (Academic Press, Burlington, MA, 2009).Lustig, M., Donoho, D. & Pauly, J. M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007).Davies, M., Puy, G., Vandergheynst, P. & Wiaux, Y. A compressed sensing framework for magnetic resonance fingerprinting. SIAM J. Imaging Sci. 7, 2623–2656 (2014).Wiaux, Y., Puy, G., Scaife, A. M. M. & Vandergheynst, P. Compressed sensing imaging techniques in radio interferometry. Mon. Not. R. Astron. Soc. 395, 1733–1742 (2009).Carrillo, R. E., McEwen, J. D. & Wiaux, Y. Sparsity averaging reweighted analysis (SARA): a novel algorithm for radio-interferometric imaging. Mon. Not. R. Astron. Soc. 426, 1223–1234 (2012).Katz, O., Bromberg, Y. & Silberberg, Y. Compressive ghost imaging. Appl. Phys. Lett. 95, 131110 (2009).Sun, B., Welsh, S. S., Edgar, M. P., Shapiro, J. H. & Padgett, M. J. Normalized ghost imaging. Opt. Express 20, 16892–16901 (2012).Kim, M., Park, C., Rodriguez, C., Park, Y. & Cho, Y.-H. Superresolution imaging with optical fluctuation using speckle patterns illumination. Sci. Rep. 5, 16525 (2015).Combettes, P. L. & Pesquet, J. -C. in Fixed-Point Algorithms for Inverse Problems in Science and Engineering (Springer, New York, 2011).Komodakis, N. & Pesquet, J.-C. Playing with duality: an overview of recent primal dual approaches for solving large-scale optimization problems. IEEE Signal Proc. Mag. 32, 31–54 (2015).Chandrasekharan, H. K. et al. Multiplexed single-mode wavelength-to-time mapping of multimode light. Nat. Commun. 8, 14080 (2017).Wadsworth, W. J. et al. Very high numerical aperture fibers. Photon. Technol. Lett. 16, 843–845 (2004).Pesquet, J.-C. & Repetti, A. A class of randomized primal-dual algorithms for distributed optimization. J. Nonlinear Convex Anal. 16, 2353–2490 (2015).Chambolle, A., Ehrhardt, M. J., Richtárik, P. & Schönlieb, C.-B. Stochastic primal-dual hybrid gradient algorithm with arbitrary sampling and imaging applications. SIAM J. Optim. 28, 2783–2808 (2018).Bolte, J., Sabach, S. & Teboulle, M. Proximal alternating linearized minimization for nonconvex and nonsmooth problems. Math. Program. 146, 459–494 (2014).Chouzenoux, E., Pesquet, J.-C. & Repetti, A. A block coordinate variable metric forward-backward algorithm. J. Glob. Optim. 66, 457–485 (2016).Flusberg, B. A. et al. Fiber-optic fluorescence imaging. Nat. Methods 2, 941–950 (2005).Tsvirkun, V. et al. Bending-induced inter-core group delays in multicore fibers. Opt. Express 25, 31863–31875 (2017).Candès, E. J., Wakin, M. B. & Boyd, S. Enhancing sparsity by reweighted l1 minimization. J. Fourier Anal. Appl. 14, 877–905 (2008).Condat, L. A primal–dual splitting method for convex optimization involving lipschitzian, proximable and linear composite terms. J. Optim. Theory Appl. 158, 460–479 (2013).Vu, B. C. A splitting algorithm for dual monotone inclusions involving cocoercive operators. Adv. Comp. Math. 38, 667–681 (2013)

    Seeing through chaos in multimode fibres

    No full text
    In a similar fashion to diffusers or other highly scattering media, multimode fibres deliver coherent light signals in the form of apparently random speckled patterns. In contrast to other optically random environments, multimode fibres feature remarkably faithful cylindrical symmetry. Our experimental studies challenge the commonly held notion that classifies multimode fibres as unpredictable optical systems. Instead, we demonstrate that commercially available multimode fibres are capable of performing as extremely precise optical components. We show that, with a sufficiently accurate theoretical model, light propagation within straight or even significantly deformed segments of multimode fibres may be predicted up to distances in excess of hundreds of millimetres. Harnessing this newly discovered predictability in imaging, we demonstrate the unparalleled power of multimode fibre-based endoscopes, which offer exceptional performance both in terms of resolution and instrument footprint. These results thus pave the way for numerous exciting applications, including high-quality imaging deep inside motile organisms.7 page(s

    Review article: The pharmacokinetics and pharmacodynamics of drugs used in inflammatory bowel disease treatment

    No full text
    corecore