In the conventional diffraction tomography a linear array is used to receive forward scattered field. Then a standard algorithm like back propagation or Fourier domain interpolation is used for reconstruction of the object. A circular array which captures both forward and backward scattered field has been proposed. A new theorem is proposed, which states that the scattered field measured with a large circular array surrounding the object is proportional to the Fourier transform of the object profile taken on the circumference of a circle of radius equa1 to the wave number and centered at (-k0 cosZO, -k0 sin Z0). The circular array outperforms in two counts. Firstly, a larger bandwidth of Fourier transform is used for reconstruction. Secondly, in circular array since the scattered field itself is related to the object Fourier transform, the reconstruction is free from the errors induced by finite array size. The effect of broad band illumination has been studied. A fewer number of illuminations appear to produce a reconstruction which is possible only with a large number of illuminations but narrow band illumination. Thus a trade off between the number of illumination angles and the bandwidth of the source exists.

Thesis (M.Sc. (Chemistry))--University of Pretoria, 2000. / Summaries in Afrikaans and English. Also available online.

The turbulent atmosphere degrades images of objects viewed through it by introducing random amplitude and phase errors into the optical wavefront. Various methods have been devised to obtain true images of such objects, including the shift-and-add method, which is examined in detail in this work. It is shown theoretically that shift-and-add processing may preserve diffraction-limited information in the resulting image, both in the point source and extended object cases, and the probability of ghost peaks in the case of an object consisting of two point sources is discussed. Also, a convergence rate for the shift-and-add algorithm is established and simulation results are presented. The combination of shift-and-add processing and Wiener filtering is shown to provide excellent image restorations.

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The behavior of digital cross-correlation algorithms as applied to image matching problems is examined in terms of the relationship between measureable image properties and algorithm characteristics. Statistical image quality measures are developed which could be employed in a preprocessor to predict the performance of automatic stereo-compilation equipment. The measures include a quantity derived from the Cramer-Rao lower bound on the variance of any unbiased parameter estimator, various contrast measures such as variance, contrast modulation, and median absolute deviation, and a stationarity detector related to the variance gradient. These measures are based on image and correlator models which describe the behavior of correlation processors under conditions of low image contrast or signal-to-noise ratio, geometric distortion, and image non-stationarity. Computer simulations using synthetic imagery were performed to verify the various models, and indicate the potential for the use of image quality measures in the predicting of correlation behavior. Implications of the models in terms of correlation processor design and implementation are discussed.

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A major problem in robotics vision is the segmentation of images of natural scenes in order to understand their content. This thesis presents a new solution to the image segmentation problem that is based on the design of a rule-based expert system. General knowledge about low level properties of an image is formulated into production rules. A number of processes employ the rules to segment the image into uniform regions and connected lines. In addition to the knowledge rules, a set of control rules are also employed. These include meta-rules that embody inferences about the order in which the knowledge rules are matched. They also include focus of attention rules that determine the path of processing within the image. A third set of rules contains the strategy rules which are data-driven inferences about the control rules. They dynamically modify the processing strategy. Different rule ordering and focus of attention strategies are selected according to a set of performance parameters. These measure the quality of the segmentation output at any point in time. Experiments with the knowledge rules resulted in an optimal set based on output quality and processing efficiency. Overall system performance is shown to be qualitatively and quantitatively superior to previous segmentation algorithms.

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