Computational Digital Inline Holography for In Situ Particle Tracking and Characterization

University of Minnesota Ph.D. dissertation.May 2020. Major: Mechanical Engineering. Advisor: Jiarong Hong. 1 computer file (PDF); xii, 130 pages.Digital inline holography (DIH) is a powerful single-camera 3D microscopic imaging tool that is able to digitally refocus a recorded image to reconstruct the 3D field of view. Compared to other single-camera techniques, DIH has a much larger depth of field in which objects can be seen, leading to drastically increased sampling volumes. Many particle features can be measured with DIH including size, shape, refractive index, identity, and motion. However, DIH has traditionally been limited by challenges related to the difficulty of accurately and quickly processing holographic images. In this thesis, I present technical developments focused on the digital processing of holographic images that are intended to alleviate these challenges and enable the application of DIH to new measurements. Specifically, a new approach for hologram reconstruction -- regularized holographic volume reconstruction (RIHVR) -- is introduced. This method is able to produce substantially noise-free reconstructions of particle fields. A data-driven approach to predictive particle tracking is also introduced in order to enable increased particle concentrations for particle tracking velocimetry applications. Each of these developments is validated using synthetic data and experimental demonstrations. Three applications of holographic imaging are presented to demonstrate the broad applicability of the method. The effect of temperature on the density of colonial cyanobacteria is identified by measuring the buoyant velocity and size of individual colonies. This could lead to better modelling of toxic algal blooms. Another type of algae, \emph{Dunaliella primolecta}, is useful and can be farmed for materials used in nutritional supplements, pharmaceuticals, and biodiesel. DIH is used to identify behavior signatures that could be used as indicators of optimal lipid production. This could enable optimal harvest timing leading to improved biodiesel yield. Finally, a low-cost miniature underwater holographic microscope was developed for \emph{in situ} field applications. This microscope is paired with a robotic platform to enable autonomous exploration of lakes or other aquatic environments. Despite its handheld size, the sensor is able to perform real-time particle concentration measurements using a deep neural network. The recorded images can also be used to identify the type of microorganisms found in the water