Applying Space-Variant Point Spread Function to Three-Dimensional Reconstruction of Fluorescence Microscopic Images

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.

[1]  Gilles Aubert,et al.  Blind restoration of confocal microscopy images in presence of a depth-variant blur and Poisson noise , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[2]  L. Lucy.  An iterative technique for the rectification of observed distributions , 1974 .

[3]  Yu Wang,et al.  The Depth-Variant Image Restoration Based on Hopfield Neural Network , 2007, Third International Conference on Natural Computation (ICNC 2007).

[4]  Stuart Jefferies,et al.  A computational method for the restoration of images with an unknown, spatially-varying blur. , 2006, Optics express.

[5]  Peter J. Verveer,et al.  Efficient superresolution restoration algorithms using maximum a posteriori estimations with application to fluorescence microscopy , 1997 .

[6]  J. Zerubia,et al.  3D Microscopy Deconvolution using Richardson-Lucy Algorithm with Total Variation Regularization , 2004 .

[7]  Thierry Blu,et al.  Accurate 3D PSF estimation from a wide-field microscopy image , 2018, 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018).

[8]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[9]  L. J. Thomas,et al.  Regularized linear method for reconstruction of three-dimensional microscopic objects from optical sections. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[10]  Chrysanthe Preza,et al.  Depth-variant maximum-likelihood restoration for three-dimensional fluorescence microscopy. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Josiane Zerubia,et al.  A deconvolution method for confocal microscopy with total variation regularization , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[12]  Gilles Aubert,et al.  Space Variant Blind Image Restoration , 2014, SIAM J. Imaging Sci..

[13]  Michael Liebling,et al.  Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[14]  Josiane Zerubia,et al.  Richardson–Lucy algorithm with total variation regularization for 3D confocal microscope deconvolution , 2006, Microscopy research and technique.

[15]  Chrysanthe Preza,et al.  Space-variant image formation for 3D fluorescence microscopy using a computationally efficient block-based model , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[16]  D S Biggs,et al.  Acceleration of iterative image restoration algorithms. , 1997, Applied optics.

[17]  T J Holmes,et al.  Blind deconvolution of quantum-limited incoherent imagery: maximum-likelihood approach. , 1992, Journal of the Optical Society of America. A, Optics and image science.

[18]  J. Zerubia,et al.  Gaussian approximations of fluorescence microscope point-spread function models. , 2007, Applied optics.