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J Opt Soc Am A Opt Image Sci Vis. 2007 Jun;24(6):1580-600.

AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data.

Author information

  • 1Graduate Group in Biophysics and Department of Biochemistry and Biophysics, University of California, San Francisco 94143-2240, USA. erik@freshboom.com

Abstract

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.

PMID:
17491626
[PubMed - indexed for MEDLINE]
PMCID:
PMC3166524
Free PMC Article
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