A Parallel Product-Convolution approach for representing the depth varying Point Spread Functions in 3D widefield microscopy based on principal component analysis
Optics Express, Vol. 18, Issue 7, pp. 6461-6476 (2010)
http://dx.doi.org/10.1364/OE.18.006461
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Abstract
We address the problem of computational representation of image formation in 3D widefield fluorescence microscopy with depth varying spherical aberrations. We first represent 3D depth-dependent point spread functions (PSFs) as a weighted sum of basis functions that are obtained by principal component analysis (PCA) of experimental data. This representation is then used to derive an approximating structure that compactly expresses the depth variant response as a sum of few depth invariant convolutions pre-multiplied by a set of 1D depth functions, where the convolving functions are the PCA-derived basis functions. The model offers an efficient and convenient trade-off between complexity and accuracy. For a given number of approximating PSFs, the proposed method results in a much better accuracy than the strata based approximation scheme that is currently used in the literature. In addition to yielding better accuracy, the proposed methods automatically eliminate the noise in the measured PSFs.
© 2010 Optical Society of America
OCIS Codes
(110.0110) Imaging systems : Imaging systems
(110.0180) Imaging systems : Microscopy
(110.6880) Imaging systems : Three-dimensional image acquisition
ToC Category:
Imaging Systems
History
Original Manuscript: January 8, 2010
Revised Manuscript: March 5, 2010
Manuscript Accepted: March 5, 2010
Published: March 15, 2010
Virtual Issues
Vol. 5, Iss. 7 Virtual Journal for Biomedical Optics
Citation
Muthuvel Arigovindan, Joshua Shaevitz, John McGowan, John W. Sedat, and David 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, 6461-6476 (2010)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-18-7-6461
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