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Recently, it is shown that graph cuts algorithms can be used to solve some variational image restoration problems, especially connected with noise removal and segmentation. For very large size images, the usage for memory and computation increases dramatically. We propose a domain decomposition method with graph cuts algorithms. We show that the new approach costs effective both for memory and computation. Experiments with large size 2D and 3D data are supplied to show the efficiency of the algorithms.
}, issn = {2617-8710}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnam/678.html} }Recently, it is shown that graph cuts algorithms can be used to solve some variational image restoration problems, especially connected with noise removal and segmentation. For very large size images, the usage for memory and computation increases dramatically. We propose a domain decomposition method with graph cuts algorithms. We show that the new approach costs effective both for memory and computation. Experiments with large size 2D and 3D data are supplied to show the efficiency of the algorithms.