Abstract / truncated to 115 words (read the full abstract)

The solution to many image restoration and reconstruction problems is often defined as the minimizer of a penalized criterion that accounts simultaneously for the data and the prior. This thesis deals more specifically with the minimization of edge-preserving penalized criteria. We focus on algorithms for large-scale problems. The minimization of penalized criteria can be addressed using a half-quadratic approach (HQ). Converging HQ algorithms have been proposed. However, their numerical cost is generally too high for large-scale problems. An alternative is to implement inexact HQ algorithms. Nonlinear conjugate gradient algorithms can also be considered using scalar HQ algorithms for the line search (NLCG+HQ1D). Some issues on the convergence of the aforementioned algorithms remained open until now. ... toggle 10 keywords

inverse problems image restoration and reconstruction deconvolution bayesian framework penalized criterion optimization convergence half-quadratic algorithms nonlinear conjugate gradient methods ultrasonic nondestructive testing


Labat, Christian
IRCCyN, Nantes, France
Publication Year
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Jan. 6, 2009

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