Multiview deblurring for 3-D images from light-sheet-based fluorescence microscopy.
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.
Chair of Pattern Recognition and Image Processing, Department of Computer Science, University of Freiburg, Freiburg, Germany. temerina@informatik.uni-freiburg.de
Maja
M
Temerinac-Ott
Olaf
O
Ronneberger
Peter
P
Ochs
Wolfgang
W
Driever
Thomas
T
Brox
Hans
H
Burkhardt
D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity