{"id":{"repo_id":"rgu","oai_identifier":"oai:rgu-repository.worktribe.com:2807297"},"canonical_url":"https://search.dev.ndltd.org/etd/rgu/oai:rgu-repository.worktribe.com:2807297","repository":{"repo_id":"rgu","name":"Robert Gordon University","base_url":"https://rgu-repository.worktribe.com/oaiprovider"},"display":{"title":"Side scan sonar image formation, restoration and modelling.","abstract":"The research described in this thesis was carried out in collaboration with Geoteam - Wimpol UK Ltd., between the October 1992 and October 1995 at the Robert Gordon University in Aberdeen. The work deals principally with the development of processing algorithms for sonar image enhancement. The thesis is divided into two principal parts. In the first part the statistical properties of side scan sonar images are analysed. Then, based on this analysis, a Maximum A Posteriori (MAP) formulation of the sonar image restoration problem is proposed. The aim of the restoration being to remove degradations such as speckle, blur and noise from the sonar image and hence to produce an estimate of the underlying reflection strength. Several different approaches to computing the MAP estimator are examined including the computationally efficient Iterative Conditional Modes (ICM) algorithm and the globally optimal Stochastic Relaxation (SR) algorithm. A linearized MAP formulation is also proposed which allows the development of fast Discrete Fourier Transform (DFT) and gradient based algorithms. The second part of the thesis deals with the correction of platform motion distortion. A novel noncausal MAP formulation of the problem is proposed and the use of the ICM and SR algorithms in the estimation of the motion parameters is described. A fast DFT based estimator, based on a linearized formulation of the problem, is also described. 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A novel noncausal MAP formulation of the problem is proposed and the use of the ICM and SR algorithms in the estimation of the motion parameters is described. A fast DFT based estimator, based on a linearized formulation of the problem, is also described. In the final section the problem of identifying the coefficients of the statistical models required to implement the MAP estimators is also discussed. The application of Maximum Likelihood (ML) estimation techniques to the problem is illustrated. In particular two iterative approaches based on the ICM and Expectation Maximisation (EM) algorithms are described."]},{"key":"dc:title","label":"Title","values":["Side scan sonar image formation, restoration and modelling."]}]}],"canonical_facts":{"dc:contributor.advisor":["S. 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