Multiview Deblurring for 3-D Images from Light-Sheet-Based Fluorescence Microscopy

• Published in: IEEE transactions on image processing Vol. 21; no. 4; pp. 1863 - 1873
• Main Authors: Temerinac-Ott, M., Ronneberger, O., Ochs, P., Driever, W., Brox, T., Burkhardt, H.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.04.2012
• Subjects: 3-D images; Algorithms; Artificial Intelligence; Deblurring with spatially variant point spread function (PSF); deconvolution; Estimation; fluorescence microscopy; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Image restoration; Imaging, Three-Dimensional - methods; Kernel; L1-regularization; Microscopy; Microscopy, Fluorescence - methods; Pattern Recognition, Automated - methods; PSF estimation; registration with irregularly placed point markers; Reproducibility of Results; Sensitivity and Specificity; Three dimensional displays; tomography; Vectors
• ISSN: 1057-7149; 1941-0042; 1941-0042
• DOI: 10.1109/TIP.2011.2181528

We propose an algorithm for 3-D multiview deblurring using spatially variant point spread functions (PSFs). The algorithm is applied to multiview reconstruction of volumetric microscopy images. It includes registration and estimation of the PSFs using irregularly placed point markers (beads). We formulate multiview deblurring as an energy minimization problem subject to L1-regularization. Optimization is based on the regularized Lucy-Richardson algorithm, which we extend to deal with our more general model. The model parameters are chosen in a profound way by optimizing them on a realistic training set. We quantitatively and qualitatively compare with existing methods and show that our method provides better signal-to-noise ratio and increases the resolution of the reconstructed images.