All issues
- 2024 Vol. 16
- 2023 Vol. 15
- 2022 Vol. 14
- 2021 Vol. 13
- 2020 Vol. 12
- 2019 Vol. 11
- 2018 Vol. 10
- 2017 Vol. 9
- 2016 Vol. 8
- 2015 Vol. 7
- 2014 Vol. 6
- 2013 Vol. 5
- 2012 Vol. 4
- 2011 Vol. 3
- 2010 Vol. 2
- 2009 Vol. 1
Image noise removal method based on nonconvex total generalized variation and primal-dual algorithm
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.
Indexed in Scopus
Full-text version of the journal is also available on the web site of the scientific electronic library eLIBRARY.RU
The journal is included in the Russian Science Citation Index
The journal is included in the RSCI
International Interdisciplinary Conference "Mathematics. Computing. Education"