Before revision:  We explain how this derivation is related to the maximum likelihood regularization parameter selection [ Galatsanos and Katsaggelos, 1992 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.
After revision:  We explain how this derivation is related to the maximum likelihood regularization parameter selection [ 5 ] , which we generalize to the case where the underlying linear operator is rectangular and depends on a nonlinear parameter . A major difference in our approach is that, unlike in [ Galatsanos and Katsaggelos, 1992 ], we do not limit ourselves to the most likely regularization parameter, instead we show that due to the dependence of the problem on the nonlinear parameter, there is a great advantage in exploring all positive values of the regularization parameter.