Abstract

In this paper, we propose an alternating minimization algorithm with an automatic selection of the regularization parameter for image reconstruction of photon-counted images. By using the generalized cross-validation technique, the regularization parameter can be updated in the iterations of the alternating minimization algorithm. Experimental results show that our proposed algorithm outperforms the two existing methods, the maximum likelihood expectation maximization estimator with total variation regularization and the primal dual method, where the parameters must be set in advance.

© 2017 Optical Society of America

Selection of regularization parameter in total variation image restoration

Haiyong Liao, Fang Li, and Michael K. Ng
J. Opt. Soc. Am. A 26(11) 2311-2320 (2009)

High-order total variation-based multiplicative noise removal with spatially adapted parameter selection

Jun Liu, Ting-Zhu Huang, Zongben Xu, and Xiao-Guang Lv
J. Opt. Soc. Am. A 30(10) 1956-1966 (2013)

Image deconvolution for confocal laser scanning microscopy using constrained total variation with a gradient field

Tao He, Yasheng Sun, Jin Qi, Jie Hu, and Haiqing Huang
Appl. Opt. 58(14) 3754-3766 (2019)

Hybrid high-order nonlocal gradient sparsity regularization for Poisson image deconvolution

Tao He, Jie Hu, and Haiqing Huang
Appl. Opt. 57(35) 10243-10256 (2018)

Three-dimensional photon counting integral imaging reconstruction using penalized maximum likelihood expectation maximization

Doron Aloni, Adrian Stern, and Bahram Javidi
Opt. Express 19(20) 19681-19687 (2011)