Bardsley, Johnathan M.; Goldes, John Regularization parameter selection methods for ill-posed Poisson maximum likelihood estimation. (English) Zbl 1176.68225 Inverse Probl. 25, No. 9, Article ID 095005, 18 p. (2009). Summary: In image processing applications, image intensity is often measured via the counting of incident photons emitted by the object of interest. In such cases, image data noise is accurately modeled by a Poisson distribution. This motivates the use of Poisson maximum likelihood estimation for image reconstruction. However, when the underlying model equation is ill-posed, regularization is needed. Regularized Poisson likelihood estimation has been studied extensively by the authors, though a problem of high importance remains: the choice of the regularization parameter. We present three statistically motivated methods for choosing the regularization parameter, and numerical examples are presented to illustrate their effectiveness. Cited in 1 ReviewCited in 32 Documents MSC: 68U10 Computing methodologies for image processing 35R99 Miscellaneous topics in partial differential equations PDFBibTeX XMLCite \textit{J. M. Bardsley} and \textit{J. Goldes}, Inverse Probl. 25, No. 9, Article ID 095005, 18 p. (2009; Zbl 1176.68225) Full Text: DOI Link