Single image deconvolution with super-resolution using the SUPPOSe algorithm

Abstract

We present the results of super-resolution deconvolution of fluorescent intracellular images using the SUPPOSe algorithm. The image is acquired using a standard fluorescence microscope and a CMOs low noise high dynamic range camera. The algorithm relies in assuming that the image source can be described by an incoherent superposition of point sources and a precise measurement of the microscope point spread function (PSF). The deconvolution problem is converted into finding the number of sources and the position of the sources that maximize the similarity between the measured image and the convolution of the sources with the PSF. The maximization is performed using a genetic algorithm. A fivefold increase in resolution is shown both by inverting a synthesized artificial image and using known beads clusters. The algorithm was applied to reconstructing images from bovine pulmonary artery endothelial cells with fluorescent labels for the F-actin and microtubules. The PSF is measured using 50nm fluorescent beads being the size of the beads the final limitation in the retrieval algorithm. The algorithm is used for the reconstruction requires the precise measurements of the PSF and the noise figure of the camera. It can be applied to reconstruct the image with super-resolution down to λ/10 and also to increase the resolution using a low magnification for wide field objective.