A Parallel Product-Convolution approach for representing the depth varying Point Spread Functions in 3D widefield microscopy based on principal component analysis.
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.
Arigovindan
Muthuvel
M
Keck Advanced Microscopy Center and the Dept. of Biochem. and Biophys., University of California at San Francisco, San Francisco, CA-94158, USA. mvel@msg.ucsf.edu
Shaevitz
Joshua
J
McGowan
John
J
Sedat
John W
JW
Agard
David A
DA
D000465:Algorithms; D001703:Biophysics / Q000379:methods; D007091:Image Processing, Computer-Assisted; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008853:Microscopy / Q000379:methods; D008856:Microscopy, Fluorescence / Q000379:methods*; D015233:Models, Statistical; D055095:Optics and Photonics; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012984:Software