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Ultrasound images speckle noise removal by nonconvex hybrid overlapping group sparsity model

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Abstract

In this paper, a novel hybrid variational model is proposed for speckle noise removal. This model contains the regularization term combined the nonconvex high-order total variation (HOTV) and overlapping group sparse total variation (OGSTV) and the data fidelity term depicted by a generalized Kullback–Leibler divergence. The proposed model inherits the advantages of nonconvex HOTV regularization and overlapping group sparse regularization and can more effectively preserve the edges and simultaneously eliminate staircase artifacts. Under the framework of alternating direction method of multipliers, we develop an efficient alternating minimization algorithm by using iteratively re-weighted \(\ell _1\) algorithm, majorization–minimization algorithm, and Newton iteration algorithm to solve the corresponding iterative scheme. Numerical experiments show that the proposed model performs better in comparison with some state-of-the-art models in visual quality and certain image quality measurement.

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Acknowledgements

This work was supported by a Project of Shandong Province Higher Educational Science and Technology Program (J17KA166), by the Major Program of the National Natural Science Foundation of China (11991024), by the National Natural Science Foundation of China (61976126), by the Science and Technology Support Plan for Youth Innovation of Colleges and Universities of Shandong Province of China (2019KJI005).

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Authors and Affiliations

  1. College of Mathematics and Systems Science, Shandong University of Science and Technology, Qingdao, 266590, China

    Jianguang Zhu & Juan Wei

  2. College of Science, China University of Petroleum, Qingdao, 266580, China

    Binbin Hao

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  1. Jianguang Zhu
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  2. Juan Wei
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Correspondence to Binbin Hao.

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Zhu, J., Wei, J. & Hao, B. Ultrasound images speckle noise removal by nonconvex hybrid overlapping group sparsity model. Vis Comput 39, 4787–4799 (2023). https://doi.org/10.1007/s00371-022-02627-7

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