Image transmission through a flexible multimode fiber by deep learning

Single-shot image retrieval through a multimode fiber using a genetic algorithm

In this letter, we present a genetic algorithm-based approach for image retrieval through a multimode fiber in a reference-less system. Due to mode interference, when an image is illuminated at one side of a multimode fiber, the transmitted light forms a noise-like speckle pattern at the other end. With the use of a prior-measured transmission matrix of the fiber, a speckle pattern is calculated using a random input mask. By optimizing the input mask to achieve a high correlation coefficient of experimental and calculated patterns, the input mask is optimized into an image with high similarity…

Seeing through multimode fibers with real-valued intensity transmission matrices

Image transmission through multimode optical fibers has been an area of immense interests driven by the demand for miniature endoscopes in biomedicine and higher speed and capacity in telecommunications. Conventionally, a complex-valued transmission matrix is obtained experimentally to link the input and output light fields of a multimode fiber for image retrieval, which complicates the experimental setup and increases the computational complexity. Here, we report a simple and high-speed method for image retrieval based on our discovery of a pseudo-linearity between the input and output light …

利用输入偏振与深度学习实现多模光纤最优成像

Towards optimal multimode fiber imaging by leveraging input polarization and deep learning

该研究探究输入光偏振态对多模光纤散斑图像重建保真度的影响，设计CGAN实现不同偏振下的图像重建，在特定条件下SSIM超0.9，为高分辨率低侵入多模光纤内窥镜研发提供基础。

Multiview Scattering Scanning Imaging Confocal Microscopy through a Multimode Fiber

Confocal and multiphoton microscopy are effective techniques to obtain high-contrast images of 2-D sections within bulk tissue. However, scattering limits their application to depths only up to ~1 millimeter. Multimode fibers make excellent ultrathin endoscopes that can penetrate deep inside the tissue with minimal damage. Here, we present Multiview Scattering Scanning Imaging Confocal (MUSSIC) Microscopy that enables high signal-to-noise ratio (SNR) imaging through a multimode fiber, hence combining the optical sectioning and resolution gain of confocal microscopy with the minimally invasive …

Fast optical refocusing through multimode fiber bend using Cake-Cutting Hadamard encoding algorithm to improve robustness

Multimode fibres offer the advantages of high resolution and miniaturization over single mode fibers in the field of optical imaging. However, multimode fibre's imaging is susceptible to perturbations of MMF that can lead to secondary spatial distortions in the transmitted image. Perturbations include random disturbances in the fiber as well as environmental noise. Here, we exploit the fast focusing capability of the Cake-Cutting Hadamard coding algorithm to counteract the effects of perturbations and improve the system's robustness. Simulation shows that it can approach the theoretical enhanc…

Image reconstruction through a multimode fiber with a simple neural network architecture

Multimode fibers (MMFs) have the potential to carry complex images for endoscopy and related applications, but decoding the complex speckle patterns produced by mode-mixing and modal dispersion in MMFs is a serious challenge. Several groups have recently shown that convolutional neural networks (CNNs) can be trained to perform high-fidelity MMF image reconstruction. We find that a considerably simpler neural network architecture, the single hidden layer dense neural network, performs at least as well as previously-used CNNs in terms of image reconstruction fidelity, and is superior in terms of…