An Efficient Total Variation Minimization Method for Image Restoration

Pham, Cong Thang; Tran, Thi Thu Thao; Gamard, Guilhem

doi:10.15388/20-INFOR407

Informatica

An Efficient Total Variation Minimization Method for Image Restoration

Volume 31, Issue 3 (2020), pp. 539–560

Cong Thang Pham Thi Thu Thao Tran Guilhem Gamard

https://doi.org/10.15388/20-INFOR407

Pub. online: 15 April 2020 Type: Research Article

Open Access

Received
1 April 2019

Accepted
1 February 2020

Published
15 April 2020

Abstract

In this paper, we present an effective algorithm for solving the Poisson–Gaussian total variation model. The existence and uniqueness of solution for the mixed Poisson–Gaussian model are proved. Due to the strict convexity of the model, the split-Bregman method is employed to solve the minimization problem. Experimental results show the effectiveness of the proposed method for mixed Poisson–Gaussion noise removal. Comparison with other existing and well-known methods is provided as well.

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Biographies

Pham Cong Thang

pcthang@dut.udn.vn

C.T. Pham received his PhD degree in engineering sciences from Tula State University, Tula, Russia, in 2016. He did a postdoctoral fellowship at Centre of Deep Learning and Bayesian Methods, National Research University Higher School of Economics, Moscow, Russia. He is currently a lecturer at the Faculty of Information Technology, The University of Danang – University of Science and Technology, Danang, Vietnam. His research interests include image processing, machine learning.

Tran Thi Thu Thao

thaotran@due.udn.vn

T.T.T. Tran received a MS degree in applied mathematics and computer science from Tula State University, Tula, Russia, in 2018. She is currently a lecturer at the Faculty of Statistics and Informatics, The University of Danang – University of Economics. Her research interests include fractal, percolation, image processing, machine learning.

Gamard Guilhem

guilhem.gamard@normale.fr

G. Gamard received his PhD degree in computer science from University of Montpellier, in 2017. He is currently a member of the ENS of Lyon. His main research interests are discrete mathematics, dynamical systems, sampled signals, data and image processing.

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Keywords

total variation image restoration mixed Poisson–Gaussian noise convex optimization split-Bregman method

Funding

This research is funded by Funds for Science and Technology Development of the University of Danang under project number B2019-DN02-62.

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