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Three-dimensional (3D) reconstruction of fluorescence microscopic images is a challenging topic in the image processing, because the imaging system is very complex, and the point spread function (PSF) continuously varies along the optical axis. Generally, the more exact the PSF is, the higher the reconstruction accuracy is. An image reconstruction method is proposed for fluorescence microscopic sample based on space-variant PSF (SV-PSF) which is generated by cubic spline theory in this paper. Firstly, key PSFs are estimated by blind deconvolution algorithm at several depths of fluorescence microscopic image stack along the optical axis. Then, other PSFs are interpolated using cubic spline theory. Finally, a 3D microscopic specimen model is reconstructed by this group of SV-PSFs. The experimental results show that the proposed method is obviously superior to the method in which space-invariant (SI) PSF is used to reconstruct the simulated and real fluorescence microscopic images. Introduction Computational optical sectioning is a powerful tool for reconstructing 3D fluorescence microscopic images. Because of the existence of diffraction limitation and spherical aberration (SA) during the course of optical imaging, the fluorescence image is often degraded by blur and noise. The mathematic model for image formation is given by the following equation, N h f g    . (1) where g is the observation image, f is the unknown true image, N is additive noise, and h is the blurring operator which is decided by PSF of the system. For simplifying the computational procedure, two assumptions are almost proposed in most image restoration algorithms. One is that the process of imaging is linear. The other is that the PSF is spaceinvariant. These methods [1-2], however, usually perform well only on thin specimen, because in the actual situation the PSF is space-variant along the optical axis z [3-5]. Only on the thin sample does the PSF vary tinily. Therefore, when data obtained from thick samples is processed using these methods, artifacts often occurs. On the other hand, for estimating all PSFs in the whole object space, extensive calculation is also an intractable problem. So some approximate algorithms are proposed in which a trade-off between the computational complexity and accuracy of image reconstruction is considered. For example, Ben Hadj et al. [6] obtained SV-PSFs by a convex combination of a group of SI-PSFs. And another 537 Advances in Computer Science Research (ACRS), volume 54 International Conference on Computer Networks and Communication Technology (CNCT2016)

Fluorescence Microscopic Image Three-dimensional Reconstruction Based on Space-variant Point Spread Function