Blind Depth-variant Deconvolution of 3D Data in Wide-field Fluorescence Microscopy

This paper proposes a new deconvolution method for 3D fluorescence wide-field microscopy. Most previous methods are insufficient in terms of restoring a 3D cell structure, since a point spread function (PSF) is simply assumed as depth-invariant, whereas a PSF of microscopy changes significantly along the optical axis. A few methods that consider a depth-variant PSF have been proposed; however, they are impractical, since they are non-blind approaches that use a known PSF in a pre-measuring condition, whereas an imaging condition of a target image is different from that of the pre-measuring. To solve these problems, this paper proposes a blind approach to estimate depth-variant specimen-dependent PSF and restore 3D cell structure. It is shown by experiments on that the proposed method outperforms the previous ones in terms of suppressing axial blur. The proposed method is composed of the following three steps: First, a non-parametric averaged PSF is estimated by the Richardson Lucy algorithm, whose initial parameter is given by the central depth prediction from intensity analysis. Second, the estimated PSF is fitted to Gibson's parametric PSF model via optimization, and depth-variant PSFs are generated. Third, a 3D cell structure is restored by using a depth-variant version of a generalized expectation-maximization.