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
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.
