Unsupervised full-color cellular image reconstruction through disordered optical fiber

Recent years have witnessed the tremendous development of fusing fiber-optic imaging with supervised deep learning to enable high-quality imaging of hard-to-reach areas.Nevertheless,the supervised deep learning method imposes strict constraints on fiber-optic imaging systems,where the input objects and the fiber outputs have to be collected in pairs.To unleash the full potential of fiber-optic imaging,unsupervised image reconstruction is in demand.Unfortunately,neither optical fiber bundles nor multimode fibers can achieve a point-to-point transmission of the object with a high sampling density,as is a prerequisite for unsupervised image reconstruction.The recently proposed disordered fibers offer a new solution based on the transverse Anderson localization.Here,we demonstrate unsupervised full-color imaging with a cellular resolution through a meter-long disordered fiber in both transmission and reflection modes.The unsupervised image reconstruction consists of two stages.In the first stage,we perform a pixel-wise standardization on the fiber outputs using the statistics of the objects.In the second stage,we recover the fine details of the reconstructions through a generative adversarial network.Unsupervised image reconstruction does not need paired images,enabling a much more flexible calibration under various conditions.Our new solution achieves full-color high-fidelity cell imaging within a working distance of at least 4 mm by only collecting the fiber outputs after an initial calibration.High imaging robustness is also demonstrated when the disordered fiber is bent with a central angle of 60°.Moreover,the cross-domain generality on unseen objects is shown to be enhanced with a diversified object set.

Complete polarization control in multimode fibers with polarization and mode coupling

Multimode optical fibers have seen increasing applications in communication,imaging,high-power lasers,and amplifiers.However,inherent imperfections and environmental perturbations cause random polarization and mode mixing,causing the output polarization states to be different from the input polarization states.This difference poses a serious issue for employing polarization-sensitive techniques to control light–matter interactions or nonlinear optical processes at the distal end of a fiber probe.Here,we demonstrate complete control of polarization states for all output channels by only manipulating the spatial wavefront of a laser beam into the fiber.Arbitrary polarization states for individual output channels are generated by wavefront shaping without constraining the input polarization.The strong coupling between the spatial and polarization degrees of freedom in a multimode fiber enables full polarization control with the spatial degrees of freedom alone;thus,wavefront shaping can transform a multimode fiber into a highly efficient reconfigurable matrix of waveplates for imaging and communication applications.

Few-mode fibre-optic microwave photonic links

The fibre-optic microwave photonic link has become one of the basic building blocks for microwave photonics.Increasing the optical power at the receiver is the best way to improve all link performance metrics including gain,noise figure and dynamic range.Even though lasers can produce and photodetectors can receive optical powers on the order of a Watt or more,the power-handling capability of optical fibres is orders-of-magnitude lower.In this paper,we propose and demonstrate the use of few-mode fibres to bridge this power-handling gap,exploiting their unique features of small acousto-optic effective area,large effective areas of optical modes,as well as orthogonality and walk-off among spatial modes.Using specially designed few-mode fibres,we demonstrate order-of-magnitude improvements in link performances for single-channel and multiplexed transmission.This work represents the first step in few-mode microwave photonics.The spatial degrees of freedom can also offer other functionalities such as large,tunable delays based on modal dispersion and wavelength-independent lossless signal combining,which are indispensable in microwave photonics.

Deep-learning cell imaging through Anderson localizing optical fiber

We demonstrate a deep-learning-based fiber imaging system that can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fiber.The cell samples are illuminated by an incoherent LED light source.A deep convolutional neural network is applied to the image reconstruction process.The network training uses data generated by a setup with straight fiber at room temperature(∼20°C)but can be utilized directly for high-fidelity reconstruction of cell images that are transported through fiber with a few degrees bend or fiber with segments heated up to 50°C.In addition,cell images located several millimeters away from the bare fiber end can be transported and recovered successfully without the assistance of distal optics.We provide evidence that the trained neural network is able to transfer its learning to recover images of cells featuring very different morphologies and classes that are never“seen”during the training process.

Author Correction:Complete polarization control in multimode fibers with polarization and mode coupling

We would like to correct an incomplete sentence in the last paragraph on page 3.“With a large number of modes in the fiber,we obtain.”should be corrected to“With a large number of modes in the fiber,we obtain PER>>1.”We would like to apologize for any confusion this may have caused.