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For further information see Electrical and Computer Engineering
The focus of this course is computational methods for the reconstruction of images from incomplete and noisy data. Key concepts are use of the multidimensional Fourier transform to describe image formation, the use of a priori information to supplement incomplete data, and image reconstruction algorithms. Topics include Fourier optics, inverse problems, iterative projection algorithms, Bayesian estimation, reconstruction from projections, deconvolution, phase retrieval, and applications including computed tomography, magnetic resonance imaging and biological imaging. The course will include a Matlab assignment and a practical computational project on an application of image reconstruction.
Subject to approval of the Head of Department.