We present a randomly disordered silica-air optical fiber featuring a 28.5% air filling fraction in the structured region, and low attenuation below 1 dB per meter at visible wavelengths. The quality of images transported through this fiber is shown to be comparable to, or even better than, that of images sent through commercial multicore imaging fiber. We demonstrate robust high-quality optical image transfer through 90 cm-long fibers with disordered silica-air structure, more than an order of magnitude improvement compared to previous disordered fiber imaging distances. The effects of variations of wavelength and feature size on transported image quality are investigated experimentally.
We demonstrate a fully flexible, artifact-free, and lensless fiber-based imaging system. For the first time, this system combines image reconstruction by a trained deep neural network with low-loss image transmission through disordered glass-air Anderson localized optical fiber. We experimentally demonstrate transmission of intensity images through meter-long disordered fiber with and without fiber bending. The system provides the unique property that the training performed within a straight fiber setup can be utilized for high fidelity reconstruction of images that are transported through either straight or bent fiber making retraining for different bending situations unnecessary. In addition, high quality image transport and reconstruction is demonstrated for objects that are several millimeters away from the fiber input facet eliminating the need for additional optical elements at the distal end of the fiber. This novel imaging system shows great potential for practical applications in endoscopy including studies on freely behaving subjects.
X-ray diffraction tomography (XDT) records the spatially-resolved X-ray diffraction profile of an extended object. Compared to conventional transmission-based tomography, XDT displays high intrinsic contrast among materials of similar electron density and improves the accuracy in material identification thanks to the molecular structural information carried by diffracted photons. However, due to the weak diffraction signal, a tomographic scan covering the entire object typically requires a synchrotron facility to make the acquisition time more manageable. Imaging applications in medical and industrial settings usually do not require the examination of the entire object. Therefore, a diffraction tomography modality covering only the region of interest (ROI) and subsequent image reconstruction techniques with truncated projections are highly desirable. Here we propose a table-top diffraction tomography system that can resolve the spatially-variant diffraction form factor from internal regions within extended samples. We demonstrate that the interior reconstruction maintains the material contrast while reducing the imaging time by 6 folds. The presented method could accelerate the acquisition of XDT and be applied in portable imaging applications with a reduced radiation dose.
We demonstrate a deep-learning-based fibre imaging system which can transfer real-time artifact-free cell images through a meter-long Anderson localizing optical fibre. 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 set-up with straight fibre at room temperature (~20 °C) but can be utilized directly for high fidelity reconstruction of cell images that are transported through fibre with a few degrees bend and/or fibre with segments heated up to 50 °C. In addition, cell images located several millimeters away from the bare fibre end can be transported and recovered successfully without the assistance of any distal optics. We further evidence that the trained neural network is able to reconstruct the images of cells which are never used in the training process and feature very different morphology.
Accurate characterization of an attosecond pulse from streaking trace is an indispensable step in studying the ultrafast electron dynamics on the attosecond scale. conventional attosecond pulse retrieval methods face two major challenges: the ability to incorporate a complete physics model of the streaking process, and the ability to model the uncertainty of pulse reconstruction in the presence of noise. Here we propose a pulse retrieval method based on conditional variational generative network (cVGn) that can address both demands. instead of learning the inverse mapping from a streaking trace to a pulse profile, the CVGN models the distribution of the pulse profile conditioned on a given streaking trace measurement, and is thus capable of assessing the uncertainty of the retrieved pulses. this capability is highly desirable for low-photon level measurement, which is typical in attosecond streaking experiments in the water window X-ray range. in addition, the proposed scheme incorporates a refined physics model that considers the Coulomb-laser coupling and photoelectron angular distribution in streaking trace generation. cVGn pulse retrievals under various simulated noise levels and experimental measurement have been demonstrated. the results showed high pulse reconstruction consistency for streaking traces when peak signal-to-noise ratio (SNR) exceeds 6, which could serve as a reference for future learning-based attosecond pulse retrieval. The generation of isolated attosecond extreme ultraviolet (XUV)/soft X-ray pulses is a milestone toward investigating the ultrafast electron dynamics on its natural time scale. Accurate temporal and spectral characterization of these XUV pulses is a crucial step in attosecond pump-probe experiments 1. Because the spectrum of the XUV pulses can be measured relatively easily with a spectrometer, knowing its spectral phase will enable a complete reconstruction of the XUV pulse in both time-and frequency-domain. Adapted from an iterative femtosecond pulse retrieval method FROG (Frequency Resolved Optical Gating), Frequency-resolved optical gating for complete reconstruction of attosecond bursts (FROG-CRAB) has been widely used for phase retrieval from attosecond streaking traces 2. Compared to its femtosecond counterpart FROG, attosecond phase retrieval suffers from both experimental and theoretical challenges. The photon flux of attosecond soft X-ray is much lower than that in a typical FROG experiment, giving rise to higher statistic noise in the streaking trace. Currently the effect of noise on the error and uncertainty of retrieved pulse is yet to be understood, and there lacks a guideline on the signal-to-noise ratio (SNR) requirement for accurate pulse retrieval. In addition, phase retrieval of ultra-broadband XUV/X-ray pulses requires a more thorough theoretic description of the photoelectron wave packet during the streaking process, including its energy and angular distribution, as well as its interaction with the laser field, which are either simplified or omitted in exi...
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