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ЧИСЛЕННЫЕ МЕТОДЫ И ОСНОВЫ ИХ РЕАЛИЗАЦИИ
Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm
C. Phama, T. Tranb, H. Danga a The University of Danang — University of Science and Technology,
54 Ngyen Luong Bang st., Danang, 550000, Vietnam
b The University of Danang — University of Economics,
71 Ngu Hanh Son st., Danang, 550000, Vietnam
Аннотация:
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.
Поступила в редакцию: 01.03.2023 Исправленный вариант: 23.03.2023 Принята в печать: 10.05.2023
Образец цитирования:
C. Pham, T. Tran, H. Dang, “Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm”, Компьютерные исследования и моделирование, 15:3 (2023), 527–541
Образцы ссылок на эту страницу:
https://www.mathnet.ru/rus/crm1074 https://www.mathnet.ru/rus/crm/v15/i3/p527
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