Open AccessPoisson noisy image restoration via overlapping group sparse and nonconvex second-order total variation priors
Author(s) -
Kyongson Jon,
Jun Li,
Xiao-Guang Lv,
Wensheng Zhu
Publication year - 2021
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0250260
Subject(s) - deblurring , image restoration , prior probability , poisson distribution , image (mathematics) , mathematics , minification , algorithm , mathematical optimization , quadratic equation , noise reduction , computer science , total variation denoising , majorization , iterative reconstruction , pattern recognition (psychology) , artificial intelligence , image processing , bayesian probability , statistics , combinatorics , geometry
The restoration of the Poisson noisy images is an essential task in many imaging applications due to the uncertainty of the number of discrete particles incident on the image sensor. In this paper, we consider utilizing a hybrid regularizer for Poisson noisy image restoration. The proposed regularizer, which combines the overlapping group sparse (OGS) total variation with the high-order nonconvex total variation, can alleviate the staircase artifacts while preserving the original sharp edges. We use the framework of the alternating direction method of multipliers to design an efficient minimization algorithm for the proposed model. Since the objective function is the sum of the non-quadratic log-likelihood and nonconvex nondifferentiable regularizer, we propose to solve the intractable subproblems by the majorization-minimization (MM) method and the iteratively reweighted least squares (IRLS) algorithm, respectively. Numerical experiments show the efficiency of the proposed method for Poissonian image restoration including denoising and deblurring.