High‐Speed All‐Fiber Micro‐Imaging with Large Depth of Field

Abstract

Single fiber imaging has evolved into a powerful method for detecting minute objects in narrow spaces. However, existing systems are not conducive to imaging dynamic objects at depth due to their bulky probes, time-consuming scanning acquisition methods, and transmissive illumination mode. Minimally invasive reflection mode imaging with high spatial and temporal resolution remains an open challenge. Here, a precise and high-speed imaging scheme without scanning is proposed. Multimode fiber imaging technology is incorporated into an all-fiber aberration-free precision detection system. High temporal resolution (5000 fps) detection of tiny natural scenes is experimentally realized by optimizing the approximation of the inverse transmission matrix in a simple and compact setup. The system can display the detected screen in real-time and the computational imaging with a large depth of field (1 mm) is enabled by jointly learning. The recovery results are superior compared to typical deep neural networks. The demonstrated scheme offers a new possibility for many applications, for example, microendoscopy, all-optical computing, and remote high-speed video transmission.

Functionalizing nanophotonic structures with 2D van der Waals materials

“…As the cornerstone of modern communication system, optical fibers lay the foundation of information communication technology, 240 and have been studied for the past few decades with extensions to many fields. [241][242][243] Under the extensive exploration of graphene 244 and other 2D materials, 31,245 numerous fiber optic devices based on van der Waals materials have been developed throughout the years as a result of their fascinating physical features. 64,196,246 Graphene fiber is a typical instance which incorporates graphene to an optical fiber to couple with the propagating light signals inside.…”

“…247 Compared with the case of integrated dielectric waveguides, 2D materialscoupled optical fibers can have much a longer interaction length between the guided electromagnetic field and 2D materials 64,248 for nonlinear harmonic generation, 249 sensors, 250 mode convertors, 240 and imaging. [241][242][243] By harvesting the giant planar anisotropy of graphene, a polarization controller based on graphene has been prototyped that profits from distinct responses upon TE and TM polarized waves by integrating graphene into the side-polished fiber (Fig. 2h), [251][252][253] and microfiber, [254][255][256] as well as other 2D materials sharing similar properties such as BP [257][258][259] and ReS 2 .…”

Real time full-color imaging in a Meta-optical fiber endoscope

“…Some of these solutions include lensless and computational imaging with single fibers [7][8][9] or coherent fiber bundles [10][11][12][13]. However, these are typically limited to a short working distance and often extremely sensitive to bending and twisting of the optical fiber, affecting, or even precluding accurate computational reconstruction.…”

Anti-perturbation Multimode Fiber Imaging Based on the Active Measurement of the Fiber Configuration

“…Based on trained neural networks, real-time or even ultrafast imaging can be achieved. 24,25 Deep learning can also be used to improve the imaging quality through dynamically perturbed MMF. 26−29 Traditional methods of MMF anti-perturbation imaging using deep learning usually require collecting a large number of object−speckle pairs under different fiber configurations for neural network training.…”

“…Recently, deep learning-based approaches have been demonstrated for image transmission through MMFs. − MMF imaging using deep learning typically uses spatial light modulators to generate the object patterns and record the corresponding speckle to establish the dataset for neural network training. Based on trained neural networks, real-time or even ultrafast imaging can be achieved. , Deep learning can also be used to improve the imaging quality through dynamically perturbed MMF. − Traditional methods of MMF anti-perturbation imaging using deep learning usually require collecting a large number of object–speckle pairs under different fiber configurations for neural network training. These methods are usually applicable to specific optical fiber configurations, and the realization of MMF imaging in unknown configurations depends on the data acquisition in hundreds of configurations.…”