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l1 regularization is used to preserve edges or enforce sparsity in a solution to an inverse problem. We investigate the split Bregman and the majorization-minimization iterative methods that turn this nonsmooth minimization problem into a sequence of steps that include solving an -regularized minimization problem. We consider selecting the regularization parameter in the inner generalized Tikhonov regularization problems that occur at each iteration in these iterative methods. The generalized cross validation method and chi2 degrees of freedom test are extended to these inner problems. In particular, for the chi2 test this includes extending the result for problems in which the regularization operator has more rows than columns and showing how to use the -weighted generalized inverse to estimate prior information at each inner iteration. Numerical experiments for image deblurring problems demonstrate that it is more effective to select the regularization parameter automatically within the iterative schemes than to keep it fixed for all iterations. Moreover, an appropriate regularization parameter can be estimated in the early iterations and fixed to convergence.