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Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm

Pham C.T., Tran T.T., Dang H.P.
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In various applications, i. e., astronomical imaging, electron microscopy, and tomography, images are often damaged by Poisson noise. At the same time, the thermal motion leads to Gaussian noise. Therefore, in such applications, the image is usually corrupted by mixed Poisson – Gaussian noise.

In this paper, we propose a novel method for recovering images corrupted by mixed Poisson – Gaussian noise. In the proposed method, we develop a total variation-based model connected with the nonconvex function and the total generalized variation regularization, which overcomes the staircase artifacts and maintains neat edges.

Numerically, we employ the primal-dual method combined with the classical iteratively reweighted $l_1$ algorithm to solve our minimization problem. Experimental results are provided to demonstrate the superiority of our proposed model and algorithm for mixed Poisson – Gaussian removal to state-of-the-art numerical methods.

Ключевые слова: total variation, image restoration, mixed noise, minimization method
Цитата: Pham C.T., Tran T.T., Dang H.P. Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm // Компьютерные исследования и моделирование, 2023, т. 15, № 3, с. 527-541
Citation in English: Pham C.T., Tran T.T., Dang H.P. Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm // Computer Research and Modeling, 2023, vol. 15, no. 3, pp. 527-541
DOI: 10.20537/2076-7633-2023-15-3-527-541
URL: http://crm.ics.org.ru/journal/article/3330/
Creative Commons License Статья доступна по лицензии Creative Commons Attribution-NoDerivs 3.0 Unported License.

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