Automatic regularization parameter selection by generalized cross-validation for total variational Poisson noise removal

Xiongjun Zhang, Bahram Javidi, Kwok Po NG*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)D47-D51
JournalApplied Optics
Volume56
Issue number9
DOIs
Publication statusPublished - 20 Mar 2017

Scopus Subject Areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Electrical and Electronic Engineering

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