AIDA: An Adaptive Image Deconvolution Algorithm

Physics – Optics

Scientific paper

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Scientific paper

We recently described an adaptive image deconvolution algorithm (AIDA) for myopic deconvolution of multi-frame and three-dimensional data acquired through astronomical and microscopic imaging [Hom et al., J. Opt. Soc. Am. A 24, 1580 (2007)]. 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. A 21, 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. AIDA includes 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. Here, we present a gallery of results demonstrating the effectiveness of AIDA in processing planetary science images acquired using adaptive-optics systems. Offered as an open-source alternative to MISTRAL, AIDA is available for download and further development at: http://msg.ucsf.edu/AIDA. This work was supported in part by the W. M. Keck Observatory, the National Institutes of Health, NASA, the National Science Foundation Science and Technology Center for Adaptive Optics at UC-Santa Cruz, and the Howard Hughes Medical Institute.

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