ArticleJournal of the Optical Society of America. A, Optics, image science, and vision2007
AIDA: an adaptive image deconvolution algorithm with application to multi-frame and three-dimensional data.
Article in Journal of the Optical Society of America. A, Optics, image science, and vision, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Adaptive optics for optical microscopy [Invited].Biomedical optics express · 2023Review
- SPITFIR(e): a supermaneuverable algorithm for fast denoising and deconvolution of 3D fluorescence microscopy images and videos.Scientific reports · 2023Article
- Live cell microscopy: From image to insight.Biophysics reviews · 2022Review
- Applications, promises, and pitfalls of deep learning for fluorescence image reconstruction.Nature methods · 2019Review
- High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio.Proceedings of the National Academy of Sciences of the United States of America · 2013Article
- Kinetic analysis of pre-ribosome structure in vivo.RNA (New York, N.Y.) · 2012Article
- High-resolution wide-field microscopy with adaptive optics for spherical aberration correction and motionless focusing.Journal of microscopy · 2010Article
- Article
- Reducing between scanner differences in multi-center PET studies.NeuroImage · 2009Article
- Article
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6 authors.
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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.
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