S. Yuan and C. Preza, “Performance evaluation of an image estimation
method based on principal component analysis (PCA) developed for
quantitative depth-variant fluorescence microscopy imaging,” Proc. SPIE 8227, 82270H (2012).

[Crossref]

M. Arigovindan, J. Shaevitz, J. McGowan, J. W. Sedat, and D. A. Agard, “A parallel product-convolution approach for
representing the depth varying point spread functions in 3D widefield
microscopy based on principal component analysis,” Opt. Express 18(7), 6461–6476 (2010).

[Crossref]
[PubMed]

C. Preza and V. Myneni, “Quantitative depth-variant imaging for fluorescence
microscopy using the COSMOS software package,” Proc. SPIE 7570, 757003 (2010).

[Crossref]

O. Haeberlé, “Focusing of light through a stratified medium:
a practical approach for computing microscope point spread functions. Part
I: Conventional microscopy,” Opt. Commun. 216(1), 55–63 (2003).

[Crossref]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
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[Crossref]
[PubMed]

J. R. Swedlow, J. W. Sedat, and D. A. Agard, “Deconvolution in optical
microscopy,” Deconvolution of Images and
Spectra 285, 284–309 (1997).

T. J. Holmes, “Expectation-maximization restoration of band-limited,
truncated point-process intensities with application in
microscopy,” J. Opt. Soc. Am. A 6(7), 1006–1014 (1989).

[Crossref]

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
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[Crossref]
[PubMed]

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[Crossref]
[PubMed]

I. J. Good, “Non-Parametric Roughness Penalty for
Probability Densities,” Nat. Phys. Sci
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[Crossref]

M. Arigovindan, J. Shaevitz, J. McGowan, J. W. Sedat, and D. A. Agard, “A parallel product-convolution approach for
representing the depth varying point spread functions in 3D widefield
microscopy based on principal component analysis,” Opt. Express 18(7), 6461–6476 (2010).

[Crossref]
[PubMed]

J. R. Swedlow, J. W. Sedat, and D. A. Agard, “Deconvolution in optical
microscopy,” Deconvolution of Images and
Spectra 285, 284–309 (1997).

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
dimensions,” Methods Cell Biol. 30, 353–377 (1989).

[Crossref]
[PubMed]

D. A. Agard, “Optical sectioning microscopy: cellular
architecture in three dimensions,” Annu. Rev.
Biophys. Bioeng. 13(1), 191–219 (1984).

[Crossref]
[PubMed]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
microscopy,” Methods 19(3), 373–385 (1999).

[Crossref]
[PubMed]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
microscopy,” Methods 19(3), 373–385 (1999).

[Crossref]
[PubMed]

I. J. Good, “Non-Parametric Roughness Penalty for
Probability Densities,” Nat. Phys. Sci
(Lond.) 229(1), 29–30 (1971).

[Crossref]

O. Haeberlé, “Focusing of light through a stratified medium:
a practical approach for computing microscope point spread functions. Part
I: Conventional microscopy,” Opt. Commun. 216(1), 55–63 (2003).

[Crossref]

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
dimensions,” Methods Cell Biol. 30, 353–377 (1989).

[Crossref]
[PubMed]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
microscopy,” Methods 19(3), 373–385 (1999).

[Crossref]
[PubMed]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
microscopy,” Methods 19(3), 373–385 (1999).

[Crossref]
[PubMed]

C. Preza and V. Myneni, “Quantitative depth-variant imaging for fluorescence
microscopy using the COSMOS software package,” Proc. SPIE 7570, 757003 (2010).

[Crossref]

S. Yuan and C. Preza, “Performance evaluation of an image estimation
method based on principal component analysis (PCA) developed for
quantitative depth-variant fluorescence microscopy imaging,” Proc. SPIE 8227, 82270H (2012).

[Crossref]

S. Yuan and C. Preza, “Point-spread function engineering to reduce the
impact of spherical aberration on 3D computational fluorescence microscopy
imaging,” Opt. Express 19(23), 23298–23314 (2011).

[Crossref]
[PubMed]

S. Yuan and C. Preza, “3D fluorescence microscopy imaging accounting
for depth-varying point-spread functions predicted by a strata interpolation
method and a principal component analysis method,” Proc. SPIE 7904, 79040M (2011).

[Crossref]

C. Preza and V. Myneni, “Quantitative depth-variant imaging for fluorescence
microscopy using the COSMOS software package,” Proc. SPIE 7570, 757003 (2010).

[Crossref]

C. Preza and J.-A. Conchello, “Depth-variant maximum-likelihood restoration
for three-dimensional fluorescence microscopy,” J.
Opt. Soc. Am. A 21(9), 1593–1601 (2004).

[Crossref]
[PubMed]

M. Arigovindan, J. Shaevitz, J. McGowan, J. W. Sedat, and D. A. Agard, “A parallel product-convolution approach for
representing the depth varying point spread functions in 3D widefield
microscopy based on principal component analysis,” Opt. Express 18(7), 6461–6476 (2010).

[Crossref]
[PubMed]

J. R. Swedlow, J. W. Sedat, and D. A. Agard, “Deconvolution in optical
microscopy,” Deconvolution of Images and
Spectra 285, 284–309 (1997).

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
dimensions,” Methods Cell Biol. 30, 353–377 (1989).

[Crossref]
[PubMed]

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
dimensions,” Methods Cell Biol. 30, 353–377 (1989).

[Crossref]
[PubMed]

J. R. Swedlow, J. W. Sedat, and D. A. Agard, “Deconvolution in optical
microscopy,” Deconvolution of Images and
Spectra 285, 284–309 (1997).

D. A. Agard, “Optical sectioning microscopy: cellular
architecture in three dimensions,” Annu. Rev.
Biophys. Bioeng. 13(1), 191–219 (1984).

[Crossref]
[PubMed]

J. R. Swedlow, J. W. Sedat, and D. A. Agard, “Deconvolution in optical
microscopy,” Deconvolution of Images and
Spectra 285, 284–309 (1997).

J.-A. Conchello, “Superresolution and convergence properties of
the expectation-maximization algorithm for maximum-likelihood deconvolution
of incoherent images,” J. Opt. Soc. Am. A 15(10), 2609–2619 (1998).

[Crossref]
[PubMed]

T. J. Holmes, “Maximum-likelihood image restoration adapted for
noncoherent optical imaging,” J. Opt. Soc. Am.
A 5(5), 666–673 (1988).

[Crossref]

T. J. Holmes, “Expectation-maximization restoration of band-limited,
truncated point-process intensities with application in
microscopy,” J. Opt. Soc. Am. A 6(7), 1006–1014 (1989).

[Crossref]

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[Crossref]
[PubMed]

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[Crossref]

C. Preza and J.-A. Conchello, “Depth-variant maximum-likelihood restoration
for three-dimensional fluorescence microscopy,” J.
Opt. Soc. Am. A 21(9), 1593–1601 (2004).

[Crossref]
[PubMed]

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution
microscopy,” Methods 19(3), 373–385 (1999).

[Crossref]
[PubMed]

D. A. Agard, Y. Hiraoka, P. Shaw, and J. W. Sedat, “Fluorescence microscopy in three
dimensions,” Methods Cell Biol. 30, 353–377 (1989).

[Crossref]
[PubMed]

I. J. Good, “Non-Parametric Roughness Penalty for
Probability Densities,” Nat. Phys. Sci
(Lond.) 229(1), 29–30 (1971).

[Crossref]

O. Haeberlé, “Focusing of light through a stratified medium:
a practical approach for computing microscope point spread functions. Part
I: Conventional microscopy,” Opt. Commun. 216(1), 55–63 (2003).

[Crossref]

M. Arigovindan, J. Shaevitz, J. McGowan, J. W. Sedat, and D. A. Agard, “A parallel product-convolution approach for
representing the depth varying point spread functions in 3D widefield
microscopy based on principal component analysis,” Opt. Express 18(7), 6461–6476 (2010).

[Crossref]
[PubMed]

S. Yuan and C. Preza, “Point-spread function engineering to reduce the
impact of spherical aberration on 3D computational fluorescence microscopy
imaging,” Opt. Express 19(23), 23298–23314 (2011).

[Crossref]
[PubMed]

C. Preza and V. Myneni, “Quantitative depth-variant imaging for fluorescence
microscopy using the COSMOS software package,” Proc. SPIE 7570, 757003 (2010).

[Crossref]

S. Yuan and C. Preza, “Performance evaluation of an image estimation
method based on principal component analysis (PCA) developed for
quantitative depth-variant fluorescence microscopy imaging,” Proc. SPIE 8227, 82270H (2012).

[Crossref]

S. Yuan and C. Preza, “3D fluorescence microscopy imaging accounting
for depth-varying point-spread functions predicted by a strata interpolation
method and a principal component analysis method,” Proc. SPIE 7904, 79040M (2011).

[Crossref]

S. Yuan and C. Preza, “3D computational microscopy with depth-varying point-spread functions using a principal component analysis method,” in Imaging and Applied Optics, OSA Technical Digest (online), paper IM3E.4 (2013).

[Crossref]

N. Patwary and C. Preza, “Computationally tractable approach to PCA-based depth-variant PSF representation for 3D microscopy image restoration,” in Classical Optics 2014, OSA Technical Digest (online), paper CW1C.5 (2014).

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N. Patwary, “Performance Aanalysis of PCA-based Image Reconstruction in 3D Wide Field Fluorescence Microscopy (MS Thesis),” University of Memphis, https://umwa.memphis.edu/etd/index.php (2014).

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Computational Imaging Research Laboratory, Computational Optical Sectioning Microscopy Open Source (COSMOS) software package; http://cirl.memphis.edu/COSMOS.