The total variation (TV) regularization method is very attractive for various image processing applications. In order to apply the TV approach to the motion pictures, it is required to reduce the computational time of the iterative TV regularization processing. In this paper, we propose a method that accelerates the convergence speed of the Chambolle's algorithm. Our proposal is based on introduction of 4-directional TV criterion and 4-dimesional dual vector. The experimental results show that we obtain less than half iteration number and 56 % computational time compared with the original Chambolle's algorithm.

The total variation (TV) regularization method is very attractive for various image processing applications. In order to apply the TV approach to motion pictures, it is required to reduce the computational time of the iterative signal processing of the TV regularization. In this paper, we propose the diagonal TV criterion instead of conventional isotropic or anisotropic TV criteria. The experimental results show that we can obtain almost half of the iteration number both in the steepest descent algorithm and Chambolle's algorithm by utilizing the diagonal TV compared with the conventional TV criteria.

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