  In this paper we consider the computation of approximate solutions for
inverse problems in Hilbert spaces. In order to capture the special feature of
solutions, non-smooth convex functions are introduced as penalty terms. By
exploiting the Hilbert space structure of the underlying problems, we propose a
fast iterative regularization method which reduces to the classical
nonstationary iterated Tikhonov regularization when the penalty term is chosen
to be the square of norm. Each iteration of the method consists of two steps:
the first step involves only the operator from the problem while the second
step involves only the penalty term. This splitting character has the advantage
of making the computation efficient. In case the data is corrupted by noise, a
stopping rule is proposed to terminate the method and the corresponding
regularization property is established. Finally, we test the performance of the
method by reporting various numerical simulations, including the image
deblurring, the determination of source term in Poisson equation, and the
de-autoconvolution problem.
