Self-calibration of lensless holographic endoscope using programmable guide stars

Kuschmierz, Robert; Scharf, Elias; Koukourakis, Nektarios; Czarske, Jürgen

doi:10.1364/ol.43.002997

Cited by 53 publications

(33 citation statements)

References 29 publications

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“…Other schemes for light control in tissue resort to the assistance of acoustics waves [5,78] but do not achieve diffraction limited optical resolution [19]. The most broadly applicable implementation for wavefront correction takes advantage of backscattered light as for example in optical coherence tomography or related approaches [53][54][55][56][57][58][59]79]. However, ultimately the availability of weakly scattered photons is limiting the imaging depth of these methods and ways to take advantage of strongly scattered light are therefore needed.…”

Section: Discussionmentioning

confidence: 99%

Light scattering control in transmission and reflection with neural networks

2018

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Scattering often limits the controlled delivery of light in applications such as biomedical imaging, optogenetics, optical trapping, and fiber-optic communication or imaging. Such scattering can be controlled by appropriately shaping the light wavefront entering the material. Here, we develop a machine-learning approach for light control. Using pairs of binary intensity patterns and intensity measurements we train neural networks (NNs) to provide the wavefront corrections necessary to shape the beam after the scatterer. Additionally, we demonstrate that NNs can be used to find a functional relationship between transmitted and reflected speckle patterns. Establishing the validity of this relationship, we focus and scan in transmission through opaque media using reflected light. Our approach shows the versatility of NNs for light shaping, for efficiently and flexibly correcting for scattering, and in particular the feasibility of transmission control based on reflected light. * Supplementary videos available in: https://www.osapublishing.org/oe/abstract.cfm?uri=oe-26-23-30911 † These two authors contributed equally ‡

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Section: Discussionmentioning

confidence: 99%

Light scattering control in transmission and reflection with neural networks

2018

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“…This possibility has had a huge impact on several optical engineering techniques. It opened the door for coping with optical distortions in flow measurements, investigations of nonlinear phenomena in MMFs or neural networks by shaping the light in a proper way, which is called adaptive wavefront shaping (AWS) [15][16][17][18]. Within the presented work, AWS is used for an accurate mode excitation by shaping the MMF input wavefield with an SLM.…”

Section: Generating Arbitrary Light Fields Using a Phase-only Slmmentioning

confidence: 99%

Transmission Matrix Measurement of Multimode Optical Fibers by Mode-Selective Excitation Using One Spatial Light Modulator

et al. 2019

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Multimode fibers (MMF) are promising candidates to increase the data rate while reducing the space required for optical fiber networks. However, their use is hampered by mode mixing and other effects, leading to speckled output patterns. This can be overcome by measuring the transmission matrix (TM) of a multimode fiber. In this contribution, a mode-selective excitation of complex amplitudes is performed with only one phase-only spatial light modulator. The light field propagating through the fiber is measured holographically and is analyzed by a rapid decomposition method. This technique requires a small amount of measurements N, which corresponds to the degree of freedom of the fiber. The TM determines the amplitude and phase relationships of the modes, which allows us to understand the mode scrambling processes in the MMF and can be used for mode division multiplexing.

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“…Optical fibers are used in a variety of applications. On the one hand, they are used in biophotonics as an image transmitting device [1,2]. On the other hand, multimode optical fibers (MMF) are also used in information technology, where the high number of modes permits spatial multiplexing.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Computational Mode Decomposition in Optical Fibers

et al. 2020

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Multimode fibers are regarded as the key technology for the steady increase in data rates in optical communication. However, light propagation in multimode fibers is complex and can lead to distortions in the transmission of information. Therefore, strategies to control the propagation of light should be developed. These strategies include the measurement of the amplitude and phase of the light field after propagation through the fiber. This is usually done with holographic approaches. In this paper, we discuss the use of a deep neural network to determine the amplitude and phase information from simple intensity-only camera images. A new type of training was developed, which is much more robust and precise than conventional training data designs. We show that the performance of the deep neural network is comparable to digital holography, but requires significantly smaller efforts. The fast characterization of multimode fibers is particularly suitable for high-performance applications like cyberphysical systems in the internet of things.

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Self-calibration of lensless holographic endoscope using programmable guide stars

Cited by 53 publications

References 29 publications

Light scattering control in transmission and reflection with neural networks

Light scattering control in transmission and reflection with neural networks

Transmission Matrix Measurement of Multimode Optical Fibers by Mode-Selective Excitation Using One Spatial Light Modulator

Deep Learning for Computational Mode Decomposition in Optical Fibers

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