A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item Controlling light Propagation in complex media for Imaging, focusing and Brillouin measurements(2018) Edrei, Eitan Y; Scarcelli, Giuliano; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Imaging and focusing light through turbid media are two fundamental challenges of optical sciences that have attracted significant attention in recent years. Traditional optical systems such as confocal microscopy, optical coherence tomography and multi-photon microscopy utilize ballistic photons traveling in straight trajectories to generate an image; however, with increasing depth, the signal to noise ratio (SNR) decreases as the number of ballistic photons decays exponentially. In the first part of this thesis I present two novel techniques for imaging through scattering medium by decoding seemingly random scattered light patterns and demonstrate the highest resolution and acquisition speed to date. For point scanning applications I also study methods to focus light through scattering materials and report on a fundamental trade-off between the focal point intensity and the focal plane in which it is generated. In the second part of the thesis I investigate how the ability to control light propagation within turbid media can be used to enhance point scanning measurements such as Brillouin scattering spectroscopy, a technology recently developed in our lab to characterize material stiffness without contact. To do this, I first present a novel optical system (“spectral coronagraph”) which yields an improved extinction ratio when inserted into Brillouin spectrometers to enable the spectral separation in the presence of scattering or close to interfaces. Additionally, to enhance the Brillouin signal, I apply adaptive optics techniques, first developed for astronomy applications, where the incident wave front is shaped to circumvent for optical phase aberrations. Using adaptive optics, I show signal enhancement in artificial and biological samples, an important feature in the context of Brillouin microscopy to promote high SNR imaging in practical scenarios.Item Sparse and Redundant Representations for Inverse Problems and Recognition(2010) Patel, Vishal M.; Chellappa, Rama; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed transmitted and/or received electromagnetic waveforms. We demonstrate that this new imaging scheme, requires no new hardware components and allows the aperture to be compressed. Also, it presents many new applications and advantages which include strong resistance to countermesasures and interception, imaging much wider swaths and reduced on-board storage requirements. The last part of the dissertation deals with object recognition based on learning dictionaries for simultaneous sparse signal approximations and feature extraction. A dictionary is learned for each object class based on given training examples which minimize the representation error with a sparseness constraint. A novel test image is then projected onto the span of the atoms in each learned dictionary. The residual vectors along with the coefficients are then used for recognition. Applications to illumination robust face recognition and automatic target recognition are presented.