Fast spatially variant deconvolution for optical microscopy via iterative shrinkage thresholding

Deconvolution offers an effective way to improve the resolution of optical microscopy data. While fast algorithms are available when the point spread function (PSF) is shift-invariant (SI), they are not directly applicable in thick samples, where the problem is shift-variant (SV). Here, we propose a...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 2838 - 2842
Main Authors: Chacko, Nikhil, Liebling, Michael
Format: Conference Proceeding
Language:English
Published: IEEE 01.05.2014
Subjects:
Deconvolution
fast iterative shrinkage/thresholding algorithms (FISTA)
Integrated optics
Microscopy
Optical imaging
Optical microscopy
Optical signal processing
sparsity
spatially-variant de-convolution
Three-dimensional displays
wavelets
ISSN:1520-6149
Online Access:Get full text
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Summary:Deconvolution offers an effective way to improve the resolution of optical microscopy data. While fast algorithms are available when the point spread function (PSF) is shift-invariant (SI), they are not directly applicable in thick samples, where the problem is shift-variant (SV). Here, we propose a fast iterative shrinkage/thresholding 3D deconvolution method that uses different PSFs at every depth. This is realized by modeling the imaging system as a multi-rate filter-bank, with each channel corresponding to a distinct 3D PSF dependent on the position along the optical axis. The complexity associated with the thresholded Landweber update in each iteration of our SV algorithm is equivalent to that of an iteration in an SI algorithm, multiplied by the number of channels in the filter-bank. We simulated images of a set of beads embedded in an aqueous gel, using varying PSFs along the optical axis, to illustrate the effectiveness of our algorithm.
ISSN:1520-6149
DOI:10.1109/ICASSP.2014.6854118