In this work, we describe our approach of combining the most effective ideas and tools developed during the past years to build a variational 3D deconvolution system that can be successfully employed in fluorescence microscopy. In particular, the main components of our deconvolution system involve proper handling of image boundaries, choice of a regularizer that is best suited to biological images, and use of an optimization algorithm that can be efficiently implemented on graphics processing units (GPUs) and fully benefit from their massive parallel computational capabilities. We show that our system leads to very competitive results and reduces the computational time by at least one order of magnitude compared to a CPU implementation. This makes the use of advanced deconvolution techniques feasible in practice and attractive computationally.
['Sander Kromwijk', 'Stamatios Lefkimmiatis', 'Michael Unser']
High-performance 3D deconvolution of fluorescence micrographs