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

Abstract

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.