The l1-norm regularization has attracted attention for image reconstruction in computed tomography. The l0-norm of the\ngradients of an image provides a measure of the sparsity of gradients of the image. In this paper, we present a new\ncombined l1-norm and l0-norm regularization model for image reconstruction from limited projection data in computed\ntomography. We also propose an algorithm in the algebraic framework to solve the optimization effectively using the\nnonmonotone alternating direction algorithm with hard thresholding method. Numerical experiments indicate that this\nnew algorithm makes much improvement by involving l0-norm regularization.
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