Title
Optimal Multivariate Gaussian Fitting with Applications to PSF Modeling in Two-Photon Microscopy Imaging
Abstract
Fitting Gaussian functions to empirical data is a crucial task in a variety of scientific applications, especially in image processing. However, most of the existing approaches for performing such fitting are restricted to two dimensions and they cannot be easily extended to higher dimensions. Moreover, they are usually based on alternating minimization schemes which benefit from few theoretical guarantees in the underlying nonconvex setting. In this paper, we provide a novel variational formulation of the multivariate Gaussian fitting problem, which is applicable to any dimension and accounts for possible nonzero background and noise in the input data. The block multiconvexity of our objective function leads us to propose a proximal alternating method to minimize it in order to estimate the Gaussian shape parameters. The resulting FIGARO algorithm is shown to converge to a critical point under mild assumptions. The algorithm shows a good robustness when tested on synthetic datasets. To demonstrate the versatility of FIGARO, we also illustrate its excellent performance in the fitting of the point spread functions of experimental raw data from a two-photon fluorescence microscope.
Year
DOI
Venue
2019
10.1007/s10851-019-00884-1
Journal of Mathematical Imaging and Vision
Keywords
Field
DocType
Gaussian fitting, Kullback–Leibler divergence, Alternating minimization, Proximal methods, PSF identification, Two-photon fluorescence microscopy
Mathematical optimization,Image processing,Algorithm,Critical point (thermodynamics),Robustness (computer science),Minification,Multivariate normal distribution,Gaussian,Mathematics,Kullback–Leibler divergence,Two-photon excitation microscopy
Journal
Volume
Issue
ISSN
61
7
0924-9907
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Emilie Chouzenoux120226.37
Tim Tsz-Kit Lau200.34
Claire Lefort300.68
Jean-Christophe Pesquet41811.52