Digital holography is a method for recording holograms through the use of an optoelectronic sensor, which serves as a replacement for the use of film [1]. Through the use of coherent light, the microparticles’s characterization can be observed with this method. Yet even using partially coherent light, images of particles can be formed revealing particle shape and size at scales larger than 10 micrometers. For example, ragweed pollen sporeclusters and glass microspheres are investigated here. The holographic images results are compared with conventional optical microscope images for validation.

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Computer generated holograms have been made practical by the introduction of fast Fourier transform algorithms. The following discussion will brief the reader on the theory and concepts of optical holograms, the computer hologram, and the analogies of the computer hologram to its optical counterpart. Some of the programming aspects of the computer holograms are presented. The results of the generation of several computer holograms are presented, and conclusions drawn as to the effectiveness of this technique. Finally, some of the practical uses and advantages of the computer hologram are discussed.

Thesis (Ph.D)--College of Fine Arts, University of New South Wales, 1999. / Includes bibliographical references. Also available online.

High-pass filtering has been shown to be a promising method for edge detection in optical scanning holography. By using a circular function as a pupil for the system, the radius of the circle can be varied to block out different ranges of frequencies. Implementing this system in simulation yields an interesting result, however. As the radius increases, a singular edge can split off into two edges instead. To understand the specific conditions under which this split occurs, Airy pattern filtering and single-sided filtering were implemented to analyze the results from the original high-pass simulation. These methods were tested with different input objects to assess any common patterns. Ultimately, no definitive answer was found, as Airy pattern filtering resulted in inconsistent results across different input objects, and single-sided filtering does not completely isolate the edge. Nonetheless, the documented results may aid a future understanding of this phenomenon. / Master of Science / Holograms are three-dimensional recordings of an object, reminiscent of how a photograph records a two-dimensional image of an object. Detecting edges in images and the reconstructed images from holograms can help us identify objects within the recorded image or hologram. In computer vision, common edge detection techniques involve analyzing the image's spatial frequency, or changes in relative intensity over space. One such technique is high-pass filtering, in which lower spatial frequencies are blocked out. High-pass filtering can also be applied to holographic imaging systems. However, when applying high-pass filtering to a holographic system, detected edges can split into two as higher frequencies are filtered out. This thesis examines the conditions for why this split-edge phenomenon occurs by modifying the original recorded object and the filtering mechanism, then analyzing the resultant holograms. While the results did not give a conclusive answer, they have been documented for the purpose of further research.

Digital Holography Microscopy (DHM) is a new 3D measurement technique that exists since Charge Coupled Devices (or CCD cameras) allow to record numerically high resolution images. That opens a new door to the theory of holography discovered in 1949 by Gabor: the door that masked the world of digital hologram processing. A hologram is a usual image but that contains the complex amplitude of the light coded into intensities recorded by the camera. The complex amplitude of the light can be seen as the combination of the energy information (squared amplitude modulus) with the information of the propagation angle of the light (phase of the amplitude) for each point of the image. When the hologram is digital, this dual information associated with a diffractive model of the light propagation permits to numerically investigate back and front planes to the recorded plane of the imaging system. We understand that 3D information can be recorded by a CCD camera and the acquisition rate of this volume information is only limited by the acquisition rate of the unique camera. For each digital hologram, the numerical investigation of front and back regions to the recorded plane is a tool to numerically refocus objects appearing unfocused in the original plane acquired by the CCD. This thesis aims to develop general and robust algorithms that are devoted to automate the analysis process in the 3D space and in time of objects present in a volume studied by a specific imaging system that permits to record holograms. Indeed, the manual processing of a huge amount of holograms is not realistic and has to be automated by software implementing precise algorithms. In this thesis, the imaging system that records holograms is a Mach-Zehnder interferometer working in transmission and studied objects are either of biological nature (crystals, vesicles, cancer cells) or latex particles. We propose and test focus criteria, based on an identical focus metric, for both amplitude and phase objects. These criteria allow the determination of the best focus plane of an object when the numerical investigation is performed. The precision of the best focus plane is lower than the depth of field of the microscope. From this refocus theory, we develop object detection algorithms that build a synthetic image where objects are bright on a dark background. This detection map of objects is the first step to a fully automatic analysis of objects present in one hologram. The combination of the detection algorithm and the focus criteria allow the precise measurement of the 3D position of the objects, and of other relevant characteristics like the object surface in its focus plane, or its convexity or whatever. These extra relevant measures are carried out with a segmentation algorithm adapted to the studied objects of this thesis (opaque objects, and transparent objects in a uniform refractive index environment). The last algorithm investigated in this research work is the data association in time of objects from hologram to hologram in order to extract 3D trajectories by using the predictive Kalman filtering theory. These algorithms are the abstract bricks of two software: DHM Object Detection and Analysis software, and Kalman Tracking software. The first software is designed for both opaque and transparent objects. The term object is not defined by one other characteristic in this work, and as a consequence, the developed algorithms are very general and can be applied on various objects studied in transmission by DHM. The tracking software is adapted to the dynamic applications of the thesis, which are flows of objects. Performance and results are exposed in a specific chapter.

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