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Fast image reconstruction for fluorescence microscopy
1. M. Bertero and P. Boccacci, Introduction of Inverse Problems in Imaging (IOP, London, 1998).
4. A. Dempster, N. Laird, and D. Rubin, J. R. Stat. Soc. Ser. B (Methodol.) 39, 1 (1977).
10. B. Bianco and A. Diaspro, Cell Biophys. 15, 189 (1989).
14. P. P. Mondal and K. Rajan, Proceedings of IEEE Instrumentation and Measurement Technology Conference, (Ontario, Canada, May 17–19, 2005).
15. P. P. Mondal and K. Rajan, Proceedings of IEEE Nuclear Science Symposium and Medical Imaging Conference, (Rome, Italy, October 16–22, 2004).
18. J. A. Conchello and J. G. McNally, in Three Dimensional Microscopy: Image Acquisition and Processing III, edited by C. J. Cogswell, G. Kino, and T. Wilson, (Proceedings of SPIE, 1996), Vol. 2655, p. 199.
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Real-time image reconstruction is essential for improving the temporal resolution of fluorescence microscopy. A number of unavoidable processes such as, optical aberration, noise and scattering degrade image quality, thereby making image reconstruction an ill-posed problem. Maximum likelihood is an attractive technique for data reconstruction especially when the problem is ill-posed. Iterative nature of the maximum likelihood technique eludes real-time imaging. Here we propose and demonstrate a compute unified device architecture (CUDA) based fast computing engine for real-time 3D fluorescenceimaging. A maximum performance boost of 210× is reported. Easy availability of powerful computing engines is a boon and may accelerate to realize real-time 3D fluorescenceimaging.
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