Preprocessing spatial and temporal backgrounds to improve super-resolution fluorescence images

International audienc

A Perspective on Data Processing in Super-resolution Fluorescence Microscopy Imaging

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Superresolution method for a single wide-field image deconvolution by superposition of point sources

In this work, we present a new algorithm for wide‐field fluorescent micrsocopy deconvolution from a single acquisition without a sparsity prior, which allows the retrieval of the target function with superresolution, with a simple approach that the measured data are fit by the convolution of a superposition of virtual point sources (SUPPOSe) of equal intensity with the point spread function. The cloud of virtual point sources approximates the actual distribution of sources that can be discrete or continuous. In this manner, only the positions of the sources need to be determined. An upper bound for the uncertainty in the position of the sources was derived, which provides a criteria to distinguish real facts from eventual artefacts and distortions. Two very different experimental situations were used for the test (an artificially synthesized image and fluorescent microscopy images), showing excellent reconstructions and agreement with the predicted uncertainties, achieving up to a fivefold improvement in the resolution for the microscope. The method also provides the optimum number of sources to be used for the fit.Fil: Martinez, Sandra Rita. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Matemática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Investigaciones Matemáticas "Luis A. Santaló". Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Investigaciones Matemáticas "Luis A. Santaló"; ArgentinaFil: Toscani, Micaela. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Oscar Eduardo. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Weighted fuzzy clustering for (fuzzy) constraints in multivariate image analysis–alternating least square of hyperspectral images

International audienceIn order to investigate hyperspectral images, many techniques such as multivariate image analysis (MIA) or multivariate curve resolution–alternating least squares (MCR–ALS) can be applied. When focusing on the use of MCR–ALS, constraints are of the utmost importance for a correct resolution of the data into its individual contributions. In this article, a fuzzy clustering pattern recognition method (fuzzy C-means) is applied on experimental data in order to improve the results obtained within the MCR–ALS analysis. The big advantage of a fuzzy clustering technique over a hard clustering technique, such as k-means, is that the algorithm determines the probability of a pixel to be assigned to a component, indicating that a pixel can be part of multiple clusters (or components). This is, of course, an important property for dealing with data in which a lot of overlap between the components in the spatial direction occurs. This article deals briefly with the implementation of the constraint into the MCR–ALS algorithm and then shows the application of the constraint on an oil-in-water emulsion obtained by Raman spectroscopy, in which the different components can be decomposed in a clearer way and the interface between the oil and water bubbles becomes more visible

Sparse deconvolution of high-density super-resolution images

In wide-field super-resolution microscopy, investigating the nanoscale structure of cellular processes, and resolving fast dynamics and morphological changes in cells requires algorithms capable of working with a high-density of emissive fluorophores. Current deconvolution algorithms estimate fluorophore density by using representations of the signal that promote sparsity of the super-resolution images via an L1-norm penalty. This penalty imposes a restriction on the sum of absolute values of the estimates of emitter brightness. By implementing an L0-norm penalty – on the number of fluorophores rather than on their overall brightness – we present a penalized regression approach that can work at high-density and allows fast super-resolution imaging. We validated our approach on simulated images with densities up to 15 emitters per μm-2 and investigated total internal reflection fluorescence (TIRF) data of mitochondria in a HEK293-T cell labeled with DAKAP-Dronpa. We demonstrated super-resolution imaging of the dynamics with a resolution down to 55 nm and a 0.5 s time sampling