@article{9354c2dc33314d1aaa1dac96fb2ed25f,
title = "Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal",
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.",
author = "Xiongjun Zhang and Bahram Javidi and Ng, {Michael K.}",
note = "Funding Information: National Natural Science Foundation of China (NSFC) (11571098); Hong Kong Research Grants Council (HKRGC) (CRF C1007-15G, GRF 12302715, GRF 12306616); Hong Kong Baptist University (HKBU) (FRG2/14-15/087); National Science Foundation (NSF) (NSF/IIS-1422179). X. Zhang's research is supported in part by NSFC, and his work was done when he visited Hong Kong Baptist University. M. Ng's research is supported in part by HKBU and HKRGC. B. Javidi acknowledges support under the NSF.",
year = "2017",
month = mar,
day = "20",
doi = "10.1364/AO.56.000D47",
language = "English",
volume = "56",
pages = "D47--D51",
journal = "Applied Optics",
issn = "1559-128X",
publisher = "Optica Publishing Group",
number = "9",
}