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

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

Jun 2007

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2007josaa..24.1580h&link_type=abstract

Journal of the Optical Society of America A, vol. 24, Issue 6, pp.1580-1600

Physics

Optics

12

Active Or Adoptive Optics, Deconvolution, Image Reconstruction-Restoration, Inverse Problems, Microscopy, Three-Dimensional Microscopy

Scientific paper

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.

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