Simultaneous iterative image restoration and evaluation of the regularization parameter
Moon Gi Kang, Aggelos K. Katsaggelos · IEEE Transactions on Signal Processing · 1992
A nonlinear regularized iterative image restoration algorithm is proposed, according to which only the noise variance is assumed to be known in advance. The algorithm results from a set theoretic regularization approach, where a bound of the stabilizing functional, and therefore the regularization parameter, are updated at each iteration step. Sufficient conditions for the convergence of the algorithm are derived and experimental results are shown.>