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Visual discomfort is the adverse sensations, such as headaches and eyestrain, encountered on viewing certain stimuli. These sensations can arise under certain viewing conditions, such as stereoscopic viewing and prolonged reading of text patterns. Also, discomfort can occur as a result of viewing stimuli with certain spatial properties, including stripes and filtered noise patterns of particular spatial frequency. This thesis is an exploration of the stimulus properties causing discomfort, within the framework of two theoretical explanations. Both of the explanations relate to the stimuli being difficult for the visual system to process. The first is concerned with discomfort being the result of inefficient neural processing. Neural activity requires energy to process information, and stimuli that demand a lot of energy to be processed might be uncomfortable. The second explanation revolves around uncomfortable stimuli not being effective in driving the accommodative (focussing) response. Accommodation relies on the stimulus as a cue to drive the response effectively - an uninformative cue might result in discomfort from an uncertain accommodative response. The following research investigates both these possibilities using a combination of psychophysical experimentation, questionnaire-based surveys on non-clinical populations, and computational modelling. The implications of the work for clinical populations are also discussed.

The purpose of the work is to explore the feasibility of devising a computer system that implements the desirable effects of a photographic filter and provides an environment for colour filter design for image processing. Using conversion from RGB to the CIELUV colour space a new method for the implementation of photographic filter as a digital filter is described. A filter is implemented by converting image pixel rgb values into CIELUV (u', v') and L* values and operates using the visual wavelength values provided by the (u', v') chromaticity diagram. However, the (u', v') diagram cannot provide wavelength values for pixels that correspond to (u', v') points in the `purple line' sector of the diagram. These pixels are allocated wavelengths by means of a new wavelengths scale that makes it possible for the filter to process any pixel in a digital image. Filter transmittance data for visual spectrum wavelengths is obtained from published tables. The transmittance data for purple sector pixels is provided by a colour model of the (u', v') chromaticity diagram. The system is evaluated by means of the Macbeth ColorChecker chart and the use of physical measurements. The extension of the CIELUV diagram with an equivalent wavelength scale provides a new environment for the enhancement and manipulation of digital colour images.

In this dissertation we develop four new methods for image restoration. The common feature of all these methods is that the object estimates have a nonlinear dependence on the image data and that iterative methods of solution are needed. The restoration algorithms have been compared with some previously developed methods by means of computer simulations. The problem of restoring noisy images where the spread function is known is treated in two ways. First, this restoration problem is regarded as a constrained least squares optimization problem. Different methods of enforcing smoothness on the restoration are considered. It is shown that the use of an arc length penalty function permits better restoration of edges than can be obtained by pure quadratic penalty functions. We also treat some methods for enforcing upper and lower bounds on the restoration. The second approach taken on the known spread function restoration problem is statistical. Here we consider the image forming system as a communication channel in which the unknown object to be estimated is one member from a random ensemble. We propose a new approach to restoration based on maximum entropy methods. This new approach allows one to easily synthesize estimators to comply with various prior constraints the image restorer wishes to impose. We show how this new maximum entropy synthesis procedure relates to previous uses of maximum entropy principles for the restoration problem. The problem of restoring atmospherically degraded images is treated in Chapter 4. Here, in addition to random noise in the image, we are faced with a randomly changing spread function. We formulated two algorithms for restoration that have better noise immunity than any previously proposed methods. Both proposed methods are based on processing a series of short exposure speckle images. The first method is an ad hoc successive least squares estimation procedure which uses the second order moments of the image and the spread function discrete Fourier transforms (DFT). The second method, which performs even better than the first, is a maximum likelihood estimation algorithm to find the object's DFT. The maximum likelihood algorithm uses both the first and second moments of the transfer function and the image's DFT.

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The application of adaptive beamforming to biomedical ultrasound imaging has been an active research area in recent years. Adaptive beamforming techniques have the capability of achieving excellent resolution and sidelobe suppression, thus improving the quality of the ultrasound images. This quality improvement, however, comes at a high computational cost. The work presented in this thesis aims to answer the following basic question: Can we reduce the computational complexity of adaptive beamforming without a significant degradation of the image quality? Our objective is to explore a combination of low-complexity non-adaptive beamforming, such as the conventional Delay-and-Sum (DAS) method, with high-complexity adaptive beamforming, such as the standard Minimum-Variance Distortionless Response (MVDR) method implemented using the Generalized Sidelobe Canceller (GSC). Such a combination should have the lower computational complexity than adaptive beamforming, but it should also offer the image quality comparable to that obtained using adaptive beamforming. In addition to the adaptive GSC-based MVDR beamforming method, we also investigate the performance of the so-called Adaptive Single Snapshot Beamformer (ASSB), which is relatively unexplored in the ultrasound imaging literature. The main idea behind our approach to combining a non-adaptive beamformer with an adaptive one is based on the use of the data-dependent variable known as the coherence factor. The resulting hybrid beamforming method can be summarized as follows: For each input snapshot to be beamformed, calculate the corresponding coherence factor; if the coherence factor is below a certain threshold, use non-adaptive DAS beamforming, otherwise use adaptive (GSC-based or ASSB-based) beamforming. We have applied this simple switching scheme to the simulated B-mode ultrasound images of the 12-point and point-scatterer-cyst phantoms that are commonly used in the ultrasound imaging literature to evaluate the image quality. Our simulation results show that, in comparison to optimal high-complexity always-adaptive beamforming, our hybrid beamformer can yield significant computational savings that range from 59% to 99%, while maintaining the image quality (measured in terms of resolution and contrast) within a 5% degradation margin. / Graduate

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