Domain decomposition methods with graph cuts algorithms for total variation minimization

Recently, graph cuts algorithms have been used to solve variational image restoration problems, especially for noise removal and segmentation. Compared to time-marching PDE methods, graph cuts based methods are more efficient and able to obtain the global minimizer. However, for high resolution and large-scale images, the cost of both memory and computational time increases dramatically. In this paper, we combine the domain decomposition method and the graph cuts algorithm for solving the total variation minimizations with L (1) and L (2) fidelity term. Numerous numerical experiments on large-scale data demonstrate the proposed algorithm yield good results in terms of computational time and memory usage. Geometry of total variation regularized L-p-model Abstract: In this paper, the geometry and scale selection properties of the total variation (TV) regularized LP-model are rigorously analyzed. Some intrinsic features different from the