An adaptive image restoration algorithm based on hybrid total variation regularization

Authors

CONG THANG PHAM
THI THU THAO TRAN
HUNG VI DANG
HOAI PHUONG DANG

DOI

10.55730/1300-0632.3968

Abstract

In imaging systems, the mixed Poisson-Gaussian noise (MPGN) model can accurately describe the noise present. Total variation (TV) regularization-based methods have been widely utilized for Poisson-Gaussian removal with edge-preserving. However, TV regularization sometimes causes staircase artifacts with piecewise constants. To overcome this issue, we propose a new model in which the regularization term is represented by a combination of total variation and high-order total variation. We study the existence and uniqueness of the minimizer for the considered model. Numerically, the minimization problem can be efficiently solved by the alternating minimization method. Furthermore, we give rigorous convergence analyses of our algorithm. Experiments results are provided to demonstrate the superiority of our proposed hybrid model and algorithm for deblurring and denoising images simultaneously, in comparison with several state-of-the-art numerical algorithms.

Keywords

Total variation, image restoration, mixed noise, minimization method

First Page

1

Last Page

16

Recommended Citation

PHAM, CONG THANG; TRAN, THI THU THAO; DANG, HUNG VI; and DANG, HOAI PHUONG (2023) "An adaptive image restoration algorithm based on hybrid total variation regularization," Turkish Journal of Electrical Engineering and Computer Sciences: Vol. 31: No. 1, Article 2. https://doi.org/10.55730/1300-0632.3968
Available at: https://journals.tubitak.gov.tr/elektrik/vol31/iss1/2