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/content/aip/journal/adva/2/3/10.1063/1.4754604
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20.See supplementary materials http://dx.doi.org/10.1063/1.4754604 for the algorithm for fast computation, GPU specifications and extensive data characterization. [Supplementary Material]
http://aip.metastore.ingenta.com/content/aip/journal/adva/2/3/10.1063/1.4754604
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/content/aip/journal/adva/2/3/10.1063/1.4754604
2012-09-17
2016-02-14

Abstract

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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