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

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