Resampling the transmission matrix in an aberration-corrected Bessel mode basis

Pai, Pritam; Bosch, Jeroen; Mosk, Allard

doi:10.1364/oe.412540

Cited by 6 publications

(1 citation statement)

References 52 publications

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“…Once the TM is measured, the basis in which it is expressed can be changed for the convenience of further operations [122,123]. An excellent example of such a case relevant to this chapter is the compressed sensing of the TM, which promises a significant speed-up compared to the above-mentioned TM measurement methods [124].…”

Section: Transmission Matrix Measurementmentioning

confidence: 99%

Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond

Cao¹,

Čižmár²,

Turtaev³

et al. 2023

Adv. Opt. Photon.

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Light transport in a highly multimode fiber exhibits complex behavior in space, time, frequency and polarization, especially in the presence of mode coupling. The newly developed techniques of spatial wavefront shaping turn out to be highly suitable to harness such enormous complexity: a spatial light modulator enables precise characterization of field propagation through a multimode fiber, and by adjusting the incident wavefront it can accurately tailor the transmitted spatial pattern, temporal profile and polarization state. This unprecedented control leads to multimode fiber applications in imaging, endoscopy, optical trapping and microfabrication. Furthermore, the output speckle pattern from a multimode fiber encodes spatial, temporal, spectral and polarization properties of the input light, allowing such information to be retrieved from spatial measurements only. This article provides an overview of recent advances and breakthroughs in controlling light propagation in multimode fibers, and discusses newly emerging applications.

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Section: Transmission Matrix Measurementmentioning

confidence: 99%

Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond

Cao¹,

Čižmár²,

Turtaev³

et al. 2023

Adv. Opt. Photon.

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Digital holographic microscopy applied to 3D computer microvision by using deep neural networks

Brito Carcaño,

Cuenat,

Ahmad

et al. 2023

EPJ Web Conf.

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Deep neural networks are increasingly applied in many branches of applied science such as computer vision and image processing by increasing performances of instruments. Different deep architectures such as convolutional neural networks or Vision Transformers can be used in advanced coherent imaging techniques such as digital holography to extract various metrics such as autofocusing reconstruction distance or 3D position determination in order to target automated microscopy or real-time phase image restitution. Deep neural networks can be trained with both datasets simulated and experimental holograms, by transfer learning. Overall, the application of deep neural networks in digital holographic microscopy and 3D computer micro-vision has the potential to significantly improve the robustness and processing speed of holograms to infer and control a 3D position for applications in micro-robotics.

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Resampling the optical transmission matrix to accelerate the training of the diffractive deep neural network

Wu,

Xia,

Zhao

et al. 2024

Appl. Opt.

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The diffractive deep neural network (D2NN) enables all-optical implementation of machine learning tasks. During the training, the Rayleigh–Sommerfeld (RS) diffraction integral is employed for connecting neurons between neighboring hidden layers. The RS formula can be rewritten as a transmission matrix (TM), which allows for the parallel computation of multiple vectorized light fields. However, the TM has a large size, demanding substantial computational resources, and resulting in long training time. In this paper, we propose to resample the TM in free space based on the propagation invariant modes (PIMs), thereby reducing the size of the matrix, and accelerating the propagation simulations. This method enables the training of the large-scale D2NN with reduced memory requirements and fast speed.

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Resampling the transmission matrix in an aberration-corrected Bessel mode basis

Cited by 6 publications

References 52 publications

Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond

Controlling light propagation in multimode fibers for imaging, spectroscopy, and beyond

Digital holographic microscopy applied to 3D computer microvision by using deep neural networks

Resampling the optical transmission matrix to accelerate the training of the diffractive deep neural network

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