Novel Imaging Systems Using Nanophotonic Devices
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This dissertation demonstrates new imaging-related applications enabled by novel nanophotonic devices. I will describe two types of nanophotonic devices: hyperbolic metamaterials (HMMs) and focal stack cameras and show their applications when combined with novel algorithms.
Several methods have been proposed to use HMM for imaging beyond the diffraction limit. However, they generally suffer from high loss, and many require coherent illumination and detection. We propose a novel method for nanostructure discrimination based on HMMs. Instead of directly imaging the objects of interest, we show that nano-sized objects with different configurations have different scattering spectra when placed on the designed HMM device, which could be used as a “fingerprint” to discriminate between different nanostructures with deep-subwavelength resolution.
The second nanophotonic device I will describe is a novel focal stack camera made from recently developed transparent graphene photodetectors. It is able capture a focal stack of a scene in a single exposure. Combining with powerful deep learning algorithms, we demonstrate several applications using such focal stack data, including learning-based light field reconstruction, unsupervised depth estimation, fast and accurate 3D object tracking and secure imaging to detect manipulated images.
Chair: Professor Theodore B. Norris