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17 January 2020 Parallel implementations to accelerate the autofocus process in microscopy applications
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Several autofocus algorithms based on the analysis of image sharpness have been proposed for microscopy applications. Since autofocus functions (AFs) are computed from several images captured at different lens positions, these algorithms are considered computationally intensive. With the aim of presenting the capabilities of dedicated hardware to speed-up the autofocus process, we discuss the implementation of four AFs using, respectively, a multicore central processing unit (CPU) architecture and a graphic processing unit (GPU) card. Throughout different experiments performed on 300 image stacks previously identified with tuberculosis bacilli, the proposed implementations have allowed for the acceleration of the computation time for some AFs up to 23 times with respect to the serial version. These results show that the optimal use of multicore CPU and GPUs can be used effectively for autofocus in real-time microscopy applications.

© 2020 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2020/$28.00 © 2020 SPIE
Juan C. Valdiviezo Navarro, Francisco J. Hernandez-Lopez, and Carina Toxqui-Quitl "Parallel implementations to accelerate the autofocus process in microscopy applications," Journal of Medical Imaging 7(1), 014001 (17 January 2020).
Received: 20 August 2019; Accepted: 24 December 2019; Published: 17 January 2020


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