Application of Regularized Richardson-Lucy Algorithm for Deconvolution of Confocal Microscopy Images

Abstract

While confocal microscopes have considerably smaller contribution of out-of-focus light than widefield microscopes, the confocal images can still be enhanced by deconvolution if the optical and data acquisition effects are accounted for. Several deconvolution algorithms have been proposed for 3D microscopy. In this work we analyze the Richardson-Lucy iterative algorithm that is derived for Poisson noise and combined with total variation (TV) regularization. The influence of TV regularization on deconvolution process is determined by one parameter. However, the choice of regularization parameters is often unknown while it has considerable effect on the result of deconvolution process. The aims of this work were: to find good estimates of regularization parameter from the input; to develop an open source software package that would allow testing different deconvolution algorithms and that would be easy to use in practice. For that, we derived a formula to estimate this regularization parameter automatically from the images as the algorithm progresses. To assess the effectiveness of this algorithm, synthetic images were composed on the basis of confocal images of rat cardiomyocytes. From the analysis of deconvolved results, we have determined under which conditions our estimation of TV regularization parameter gives good results. The estimated TV regularization parameter can be monitored during deconvolution process and used as a stopping criterion. As a result, we propose a practical method to deconvolve confocal microscope images that uses estimated regularization parameter depending on the input image. We applied the deconvolution algorithm to study mitochondrial organization in rat cardiomyocytes. An open source software for deconvolving 3D images is available in http://sysbio.ioc.ee/software/.