Total variation based on the L0− and L1−norm regularizers is a popular and powerful method for computed tomography (CT) reconstruction from noisy data. However their solutions are difficult to compute because of non-differentiability and even discontinuity. In this paper, we propose smoothing L0- and L1-norm regularizers to approximate the original norms. Also, we provide two expectation maximization (EM) like iterative algorithms, which have properties similar to that of the EM algorithm, including the monotonous decrement of the cost function and the self-constraint within a non-negative space. Finally, we build the relationship between the non-local means (NLM) methods and the above methods. The simulated CT projections are used to evaluate the performance of the proposed algorithms. The results show the superiority of the new methods over the existing methods in terms of noise suppression and computational cost.