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 mi...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.展开更多
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...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.展开更多
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&g...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.展开更多
基金supported by the US National Science Foundation under Grant No.ECCS-1509361.
文摘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.
基金The authors would like to thank the valuable discussions provided by the Fiber Optics Lab at CREOL.
文摘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.
文摘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.
