Fluorescence Microscopy Deconvolution Based on Bregman Iteration and Richardson-Lucy Algorithm with TV Regularization

Fluorescence microscopy has become an important tool in biological and medical sciences for imaging thin specimen, even living ones. Due to Out-of-focus blurring and noise the acquired images are degraded and therefore it can be difficult to analyse them. In the last decade many methods have been proposed to restorate these images. One of the most popular methods to restore microscopy images is an iterative Richardson-Lucy Algorithm with Total Variation Regularization. Besides there are some new approaches based on Bregman Iteration to improve the quality of restored images in general. In this paper we formulate a new algorithm for restoring fluorescense microscope images using Bregman Iteration with special attention to the microscopy specific properties. We can proof that the quality of the restored images increases by using the I-divergence and the mean square error criteria.