This thesis aimed to investigate digital methods for identification, classification, and correction of particle images using digital holographic microscopy. The work begins with a micro-scale flow experiment employing a digital off-axis holographic microscope. To overcome noise from out-of-focus particle images due to the increase in the particle concentration, a new particle image identification algorithm with threedimensional reconstruction was introduced. It shows that individual particle images could be identified at about 62% of the expected number of particles. However, a cylindrical micro-channel used in the experiment was found as the main source of astigmatism. Following this, an automatic image classification using neural network was proposed to classify reliable and astigmatic particle images. A feed-forward backpropagation neural network with two class classifier was trained, achieving overall accuracy of 99.8%. Next, an original method of aberration correction using a prio.....

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Digital holographic microscopy (DHM) is a promising tool for a three-dimensional imaging of microscopic particles. It offers the possibility of wavefront processing by manipulating amplitude and phase of the recorded digital holograms. With a view to compensate for aberration in the reconstructed particle images, this paper discusses a new approach of aberration compensation based on complex amplitude correlation and the use of a priori information. The approach is applied to holograms of microscopic particles flowing inside a cylindrical micro-channel recorded using an off-axis digital holographic microscope. The approach results in improvements in the image and signal qualities...

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