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Image processing

University of British Columbia

In this thesis, we consider the restoration of multiple grey levels image. The problem is to clean up or restore the dirty picture, that is, to construct an estimate of the true image from a noisy picture of that true image. Following a method proposed by Meloche and Zamar (1994), we estimate the colour at each site by a function of the data available in a neighbourhood of that site. In this approach, the local characteristics of that image, that is, the frequency with which each pattern appears in the true unobserved image are particularly important. We will propose a family of unbiased estimates of the pattern distribution and the noise level which are used in the restoration process. We will use our estimates of the pattern distribution in an attempt to select the best neighbourhood shape for the restoration process.

Thesis/Dissertation

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2009-01-13

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http://hdl.handle.net/2429/3604

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University of British Columbia

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