Image Restoration by a Mixed High-Order Total Variation and Regularization Model

Jianguang Zhu, Kai Li and Binbin Hao

Mathematical Problems in Engineering, 2018, vol. 2018, 1-13

Abstract:

Total variation regularization is well-known for recovering sharp edges; however, it usually produces staircase artifacts. In this paper, in order to overcome the shortcoming of total variation regularization, we propose a new variational model combining high-order total variation regularization and regularization. The new model has separable structure which enables us to solve the involved subproblems more efficiently. We propose a fast alternating method by employing the fast iterative shrinkage-thresholding algorithm (FISTA) and the alternating direction method of multipliers (ADMM). Compared with some current state-of-the-art methods, numerical experiments show that our proposed model can significantly improve the quality of restored images and obtain higher SNR and SSIM values.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:6538610

DOI: 10.1155/2018/6538610

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