Iterative evaluation of the regularization parameter in regularized image restoration

In this paper a nonlinear regularized iterative image restoration algorithm is proposed, according to which no prior knowledge about the noise variance is assumed. The algorithm results from a set-theoretic regularization approach, where bounds of the stabilizing functional and the noise variance, which determine the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm, as well as an optimality criterion for the regularization parameter, are derived and experimental results are shown.