Averaging speckle patterns to improve the robustness of compressive multimode fiber imaging against fiber bend

Lan, Mingying; Xiang, Yangyang; Li, Junhui; Gao, Li; Liu, Yuanhang; Wang, Ziyu; Yu, Song; Wu, Guohua; Ma, Jianxin

doi:10.1364/oe.387648

Cited by 23 publications

(8 citation statements)

References 20 publications

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“…By calculating the correct series of input fields, this focused beam can be scanned over an object with a field of view defined by the numerical aperture of the fiber. The second class of methods consists of speckle imaging approaches [14][15][16][17] . With these methods, a set of speckle patterns is recorded at the distal end of a MMF, forming a measurement matrix during a calibration stage.…”

Section: Introductionmentioning

confidence: 99%

Robust real-time imaging through flexible multimode fibers

Abdulaziz¹,

Mekhail

Altmann³

et al. 2023

Preprint

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Conventional endoscopes comprise a bundle of optical fibers, associating one fiber for each pixel in the image. In principle, this can be reduced to a single multimode optical fiber (MMF), the width of a human hair, with one fiber spatial-mode per image pixel. However, images transmitted through a MMF emerge as unrecognisable speckle patterns due to dispersion and coupling between the spatial modes of the fiber. Furthermore, speckle patterns change as the fiber undergoes bending, making the use of MMFs in flexible imaging applications even more complicated. In this paper, we propose a real-time imaging system using flexible MMFs, but which is robust to bending. Our approach does not require access or feedback signal from the distal end of the fiber during imaging. We leverage a variational autoencoder (VAE) to reconstruct and classify images from the speckles and show that these images can still be recovered when the bend configuration of the fiber is changed to one that was not part of the training set. We utilize a MMF 300 mm long with a 50 um core for imaging 10x10 cm objects placed approximately at 20 cm from the fiber and the system can deal with a change in fiber bend of 50 degrees and range of movement of 8 cm.

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Section: Introductionmentioning

confidence: 99%

Robust real-time imaging through flexible multimode fibers

Abdulaziz¹,

Mekhail

Altmann³

et al. 2023

Preprint

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“…[26] Recently, more compressive sensing MMF imaging schemes were proposed toward robustness of light transport or fast imaging speed. [27][28][29][30][31][32] On the other hand, it has been widely recognized that reflected light transmitted back through the fiber is beneficial to detect fiber deformation and is promising for imaging without distal access, [33][34][35][36][37] which is critical for practical MMF imaging. One early attempt was by using a virtual coherent point light source placed at the distal fiber tip to dynamically compensate for bending, and the light was focused through a semi-flexible MMF for which the number of conformation is limited.…”

Section: Introductionmentioning

confidence: 99%

“…[ 26 ] Recently, more compressive sensing MMF imaging schemes were proposed toward robustness of light transport or fast imaging speed. [ 27–32 ]…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Enabled Scalable Calibration of a Dynamically Deformed Multimode Fiber

Fan

Wang

Ruddlesden

et al. 2022

Advanced Photonics Research

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Multimode fibers (MMF) are miniaturized, flexible, and high‐capacity information channels, promising to open up new applications in endoscopic imaging. However, precise light control through an MMF with continuous deformations is still a challenge. Here, a scalable calibration framework for a dynamically deformed MMF using deep learning is proposed. The proof‐of‐concept experiments demonstrate that the proposed continual generative adversarial model has the ability to characterize the MMF transmission states sequentially and detect the fiber deformation using proximal reflection in real‐time synchronously, allowing self‐adaptively cross‐state focusing through a semi‐flexible MMF without distal access after the scalable calibration. This framework is a continual learning scheme under extreme memory constraints where the model is able to synthesize training data and prevent forgetting the previously learned bending states. The proposed method paves the way for the experimental realization of scalable calibration of a dynamically deformed MMF.

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“…Recent research reported a speckle imaging method based on compressive sensing (CSSI), without complex wave-front shaping, the CSSI method can realize image transmission and reconstruction through a multimode fiber. [10,11,[24][25][26] However, with the limitation of sampling number, calculation time (each imaging process needs independent calculation), and pre-calibration (at the beginning of each experiment), the imaging speed of the CSSI method is difficult to meet the demand of video transmission. Therefore, a method without phase information and pre-calibration that can simulate the nonlinear relationship inside a multimode fiber is urgently needed.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7] Compared with singlemode fiber with the same external diameter, the core diameter of multimode fiber is dozens of times that of single-mode fiber, which allows multimode fibers to transmit multiple optical modes simultaneously. [8][9][10][11][12] In theory, the hundreds of micronsthick multimode fiber can replace the millimeters-thick fibers DOI: 10.1002/lpor.202200339 bundle used in endoscopes, which can image the tiny spaces that cannot be reached by the existing endoscopes. [13,14] Therefore, multimode fiber is an excellent option to promote the microminiaturization of endoscopes.…”

Section: Introductionmentioning

confidence: 99%

High Accuracy Transmission and Recognition of Complex Images through Multimode Fibers Using Deep Learning

Zhang

Chen

et al. 2022

Laser & Photonics Reviews

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Multimode fiber shows tremendous potential in promoting the microminiaturization of optical endoscopes. However, multimode transmission is quite sensitive to fiber deformations and environmental changes. High‐accuracy transmission of complex images through a multimode fiber using traditional methods remains challenging research. Deep learning, which shows enormous vitality in optical imaging, may break through this limitation. Here, a deep neural network: U‐architecture speckles imaging network (USINET) is presented to realize high accuracy reconstruction of complex images under different multimode fiber transmission conditions. Furthermore, a shallow neural network: convolutional neural network (CNN)‐architecture speckles recognition network (CSRNET) is designed to realize high accuracy recognition for multiple categories of speckles at the output of multimode fiber under different bending states. The experimental results demonstrate that the proposed networks can realize high accuracy transmission and recognition of complex images through multimode fibers, which indicates the application prospect of multimode fibers combined with deep learning in minimally invasive medicine.

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Averaging speckle patterns to improve the robustness of compressive multimode fiber imaging against fiber bend

Cited by 23 publications

References 20 publications

Robust real-time imaging through flexible multimode fibers

Robust real-time imaging through flexible multimode fibers

Deep Learning Enabled Scalable Calibration of a Dynamically Deformed Multimode Fiber

High Accuracy Transmission and Recognition of Complex Images through Multimode Fibers Using Deep Learning

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