Dissertation Title:
“Imaging Through Glass-Air Anderson Localizing Optical Fiber”
Abstract:
Anderson localization optical fiber has been a subject of intense research recently. It has a transversely random but longitudinally uniform refractive index profile. The strong scattering from the transversely disordered refractive index profiles generates thousands of guiding modes which are spatially isolated and mainly demonstrate single-mode properties. By making use of these beam transmission channels, robust and high-fidelity imaging transport can be realized. Previous research using polymer Anderson localizing optical fiber has demonstrated that its great potential in imaging applications. However, the low refractive index contrast in the random polymer fiber leads to rather large Anderson localization areas for the propagating light and, therefore, restricts the achievable image resolution. Besides, strong scattering losses of the polymer materials limit the image transport distance to a few centimeters. To obtain longer transmission lengths and better imaging qualities, glass-air Anderson localizing optical fibers (GALOFs) are desirable due to lower loss and larger refractive index contrast. Recently, we fabricate the first high air-filling fraction GALOF. We experimentally prove that our GALOF can provide bending-independent and high-quality image transport through a meter-long transmission distance. The imaging quality and resolution of GALOFs are comparable to some of the best commercial multicore imaging fiber. Moreover, both numerical calculations and experimental measurements demonstrate that a large number of localized modes in GALOF exhibit high-quality wavefronts (M2~1) and high spatial coherence making these transmission channels comparable to single-mode optical fibers. By integrating deep convolutional neural networks (DCNNs) with GALOF, we show a fully-flexible, artifact-free and lensless fiber imaging system based on laser illumination and simple binary objects for the first time. Very recently, based on a new DCNN model with a tailored design we develop an incoherent illuminated DCNN-GALOF imaging system with the capabilities to image various cell structures. With this system, artifact-free images of different types of cells can be transferred in real time. The imaging depth can reach up to several millimeters without any distal optics. Its image reconstruction process is remarkably robust with regard to external perturbations, Especially, the transfer-learning capability of the new system is confirmed by using cells of different morphology. The work presented here introduces a new platform for various practical applications, such as neuroscience research and clinical diagnosis. It is also a new cornerstone for imaging research based on waveguide devices using transverse Anderson localization.
Major: Optics and Photonics
Educational Career:
BS: 2012, Optics, Sun Yat-sen University
MS: 2014, Optics and Photonics, University of Central Florida
Committee in Charge:
Dr. Axel Schülzgen (Chair)
Dr. Rodrigo Amezcua Correa
Dr. Shuo Pang
Dr. Peter J. Delfyett
Dr. Arash Mafi
Approved for distribution by Dr. Axel Schülzgen, Committee Chair, on April 10, 2019.
The public is welcome to attend.
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