Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals

Kagie, Matthew J.; Lanterman, Aaron D.

doi:10.1364/JOSAA.35.001228

Title: Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals

Abstract

This paper addresses parameter estimation for an optical transient signal when the received data has been right-censored. We develop an expectation-maximization (EM) algorithm to estimate the amplitude of a Poisson intensity with a known shape in the presence of additive background counts, where the measurements are subject to saturation effects. We then compare the results of our algorithm with those of an EM algorithm that is unaware of the censoring.

Authors:

Kagie, Matthew J. ^[1]; Lanterman, Aaron D. ^[2]

Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Georgia Inst. of Technology, Atlanta, GA (United States). School of Electrical and Computer Engineering

Publication Date:: Fri Jun 29 00:00:00 EDT 2018

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA)

OSTI Identifier:: 1467001

Report Number(s):: SAND2017-13806J
Journal ID: ISSN 1084-7529; JOAOD6; 659680

Grant/Contract Number:: AC04-94AL85000

Resource Type:: Accepted Manuscript

Journal Name:: Journal of the Optical Society of America. A, Optics, Image Science, and Vision

Additional Journal Information:: Journal Volume: 35; Journal Issue: 7; Journal ID: ISSN 1084-7529

Publisher:: Optical Society of America (OSA)

Country of Publication:: United States

Language:: English

Subject:: 97 MATHEMATICS AND COMPUTING; 47 OTHER INSTRUMENTATION

Citation Formats


                    Kagie, Matthew J., and Lanterman, Aaron D. Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals.  United States: N. p., 2018. 
Web.  doi:10.1364/JOSAA.35.001228.

Copy to clipboard


                    Kagie, Matthew J., & Lanterman, Aaron D. Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals.  United States.  https://doi.org/10.1364/JOSAA.35.001228

Copy to clipboard


                    Kagie, Matthew J., and Lanterman, Aaron D. Fri .  
"Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals".  United States.  https://doi.org/10.1364/JOSAA.35.001228.  https://www.osti.gov/servlets/purl/1467001.

Copy to clipboard


                    
@article{osti_1467001,

  title        = {Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals},

  author       = {Kagie, Matthew J. and Lanterman, Aaron D.},

  abstractNote = {This paper addresses parameter estimation for an optical transient signal when the received data has been right-censored. We develop an expectation-maximization (EM) algorithm to estimate the amplitude of a Poisson intensity with a known shape in the presence of additive background counts, where the measurements are subject to saturation effects. We then compare the results of our algorithm with those of an EM algorithm that is unaware of the censoring.},

  doi          = {10.1364/JOSAA.35.001228},

  journal      = {Journal of the Optical Society of America. A, Optics, Image Science, and Vision},

  number       = 7,

  volume       = 35,

  place        = {United States},

  year         = {Fri Jun 29 00:00:00 EDT 2018},

  month        = {Fri Jun 29 00:00:00 EDT 2018}

}

Copy to clipboard

Journal Article:

Free Publicly Available Full Text

Accepted Manuscript (DOE)

Publisher's Version of Record

https://doi.org/10.1364/JOSAA.35.001228

Other availability

Search WorldCat to find libraries that may hold this journal

Citation Metrics:

Cited by: 1 work

Citation information provided by
Web of Science

Figures / Tables:

Fig. 1.: Summand for low noise, partially saturated, low photon count (top) and high noise, partially saturated, high photon count (bottom) scenarios.

All figures and tables (6 total)

Save / Share:

Export Metadata

Save to My Library

Works referenced in this record:

Image recovery from data acquired with a charge-coupled-device camera
journal, January 1993

Snyder, Donald L.; White, Richard L.; Hammoud, Abed M.
Journal of the Optical Society of America A, Vol. 10, Issue 5
DOI: 10.1364/JOSAA.10.001014

Minimum description length approach to detecting chemicals via their Raman spectra
journal, August 2011

Palkki, Ryan D.
Optical Engineering, Vol. 50, Issue 8
DOI: 10.1117/1.3609805

EM Algorithm for Truncated and Censored Poisson Likelihoods
journal, January 2016

Viwatwongkasem, Chukiat
Procedia Computer Science, Vol. 86
DOI: 10.1016/j.procs.2016.05.109

Compensation for readout noise in CCD images
journal, January 1995

Snyder, Donald L.; Helstrom, Carl W.; Lanterman, Aaron D.
Journal of the Optical Society of America A, Vol. 12, Issue 2
DOI: 10.1364/JOSAA.12.000272

Figures / Tables found in this record:

Figures/Tables have been extracted from DOE-funded journal article accepted manuscripts.

Similar Records in DOE PAGES and OSTI.GOV collections:

Numerical estimation of adsorption energy distributions from adsorption isotherm data with the expectation-maximization method

Technical Report Stanley, B J ; Guiochon, G ; Oak Ridge National Lab., TN

The expectation-maximization (EM) method of parameter estimation is used to calculate adsorption energy distributions of molecular probes from their adsorption isotherms. EM does not require prior knowledge of the distribution function or the isotherm, requires no smoothing of the isotherm data, and converges with high stability towards the maximum-likelihood estimate. The method is therefore robust and accurate at high iteration numbers. The EM algorithm is tested with simulated energy distributions corresponding to unimodal Gaussian, bimodal Gaussian, Poisson distributions, and the distributions resulting from Misra isotherms. Theoretical isotherms are generated from these distributions using the Langmuir model, and then chromatographic bandmore »« less
https://doi.org/10.2172/10173477

Full Text Available
Numerical estimation of adsorption energy distributions from adsorption isotherm data with the expectation-maximization method

Journal Article Stanley, B J ; Guiochon, G - Journal of Physical Chemistry; (United States)

The expectation-maximization (EM) method of parameter estimation is used to calculate adsorption energy distributions of molecular probes from their adsorption isotherms. EM does not require prior knowledge of the distribution function, or the isotherm, requires no smoothing of the isotherm data, and converges with high stability toward the maximum-likelihood estimate. The method is therefore robust and accurate at high iteration numbers. The EM algorithm is tested with simulated energy distributions corresponding to unimodel Gaussian, bimodal Gaussian, Poisson distributions, and the distributions resulting from Misra isotherms. Theoretical isotherms are generated from these distributions using the Langmuir model, and then chromatographic bandmore »« less
https://doi.org/10.1021/j100132a046
A multigrid expectation maximization reconstruction algorithm for positron emission tomography

Journal Article Ranganath, M V ; Dhawan, A P ; Mullani, N - IEEE Trans. Med. Imag.; (United States)

The problem of reconstruction in positron emission tomography (PET) is basically estimating the number of photon pairs emitted from the source. Using the concept of the maximum likelihood (ML) algorithm, the problem of reconstruction is reduced to determining an estimate of the emitter density that maximizes the probability of observing the actual detector count data over all possible emitter density distributions. A solution using this type of expectation maximization (EM) algorithm with a fixed grid size is severely handicapped by the slow convergence rate, the large computation time, and the non-uniform correction efficiency of each iteration making the algorithm verymore »« less
https://doi.org/10.1109/42.14509
Expectation-maximization algorithms for learning a finite mixture of univariate survival time distributions from partially specified class values

Thesis/Dissertation Lee, Youngrok

Heterogeneity exists on a data set when samples from di erent classes are merged into the data set. Finite mixture models can be used to represent a survival time distribution on heterogeneous patient group by the proportions of each class and by the survival time distribution within each class as well. The heterogeneous data set cannot be explicitly decomposed to homogeneous subgroups unless all the samples are precisely labeled by their origin classes; such impossibility of decomposition is a barrier to overcome for estimating nite mixture models. The expectation-maximization (EM) algorithm has been used to obtain maximum likelihood estimates ofmore »« less
https://doi.org/10.2172/1116720

Full Text Available
TH-CD-206-01: Expectation-Maximization Algorithm-Based Tissue Mixture Quantification for Perfusion MRI

Journal Article Han, H ; Xing, L ; Liang, Z ; ... - Medical Physics

Purpose: To investigate the feasibility of estimating the tissue mixture perfusions and quantifying cerebral blood flow change in arterial spin labeled (ASL) perfusion MR images. Methods: The proposed perfusion MR image analysis framework consists of 5 steps: (1) Inhomogeneity correction was performed on the T1- and T2-weighted images, which are available for each studied perfusion MR dataset. (2) We used the publicly available FSL toolbox to strip off the non-brain structures from the T1- and T2-weighted MR images. (3) We applied a multi-spectral tissue-mixture segmentation algorithm on both T1- and T2-structural MR images to roughly estimate the fraction of eachmore »« less
https://doi.org/10.1118/1.4958182

Similar Records

Title: Expectation-maximization algorithm for amplitude estimation of saturated optical transient signals

Abstract

Citation Formats

Figures / Tables:

Image recovery from data acquired with a charge-coupled-device camera journal, January 1993

Minimum description length approach to detecting chemicals via their Raman spectra journal, August 2011

EM Algorithm for Truncated and Censored Poisson Likelihoods journal, January 2016

Compensation for readout noise in CCD images journal, January 1995

Image recovery from data acquired with a charge-coupled-device camera
journal, January 1993

Minimum description length approach to detecting chemicals via their Raman spectra
journal, August 2011

EM Algorithm for Truncated and Censored Poisson Likelihoods
journal, January 2016

Compensation for readout noise in CCD images
journal, January 1995