Three-dimensional deconvolution of wide field microscopy with sparse priors: Application to zebrafish imagery

Zebra fish, as a popular experimental model organism, has been frequently used in biomedical research. For observing, analysing and recording labelled transparent features in zebra fish images, it is often efficient and convenient to adopt the fluorescence microscopy. However, the acquired z-stack images are always blurred, which makes deblurring/deconvolution critical for further image analysis. In this paper, we propose a Bayesian Maximum a-Posteriori (MAP) method with the sparse image priors to solve three-dimensional (3D) deconvolution problem for Wide Field (WF) fluorescence microscopy images from zebra fish embryos. The novel sparse image priors include a global Hyper-Laplacian model and a local smooth region mask. These two kinds of prior are deployed for preserving sharp edges and suppressing ringing artifacts, respectively. Both synthetic and real WF fluorescent zebra fish embryo data are used for evaluation. Experimental results demonstrate the potential applicability of the proposed method for 3D fluorescence microscopy images, compared with state-of-the-art 3D deconvolution algorithms.