Recently

there are many practical applications of tomographic reconstruction that consist of minimizing the sum of a residual energy and image total variation (TV). In this paper

we develop an uncoupled iterative algorithm for solving the recent popular TV-regularized CT optimization problem. Using surrogate function

we split the sum minimization scheme to the minimizing weighted least square function and TV denoising with weighted norm iteratively. In order to find the stable solution of this model

we use Chambolle’s scheme to overcome the numerical difficulty due to the non differentiability of the TV norm and use Bregman scheme to accelerate the iteration process. Experimental results show that the proposed approach outperforms some existing TV tomography methods based on the gradient descent algorithms.