AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data.
We describe an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging. AIDA is a reimplementation and extension of the MISTRAL method developed by Mugnier and co-workers and shown to yield object reconstructions with excellent edge preservation and photometric precision [J. Opt. Soc. Am. A21, 1841 (2004)]. Written in Numerical Python with calls to a robust constrained conjugate gradient method, AIDA has significantly improved run times over the original MISTRAL implementation. Included in AIDA is a scheme to automatically balance maximum-likelihood estimation and object regularization, which significantly decreases the amount of time and effort needed to generate satisfactory reconstructions. We validated AIDA using synthetic data spanning a broad range of signal-to-noise ratios and image types and demonstrated the algorithm to be effective for experimental data from adaptive optics-equipped telescope systems and wide-field microscopy.
Hom
Erik F Y
EF
Graduate Group in Biophysics and Department of Biochemistry and Biophysics, University of California, San Francisco 94143-2240, USA. erik@freshboom.com
Marchis
Franck
F
Lee
Timothy K
TK
Haase
Sebastian
S
Agard
David A
DA
Sedat
John W
JW
D000465:Algorithms; D000818:Animals; D001001:Anura / Q000033:anatomy & histology; D002875:Chromosomes; D004330:Drosophila / Q000196:embryology / Q000235:genetics; D007090:Image Interpretation, Computer-Assisted; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging; D008870:Microtubules / Q000502:physiology; D008938:Mitosis / Q000235:genetics; D008962:Models, Theoretical; D016083:Planets; D015203:Reproducibility of Results; D012568:Schizosaccharomyces / Q000502:physiology