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Parallel implementations to accelerate the autofocus process in microscopy applications.

Juan C Valdiviezo-N1, Francisco J Hernandez-Lopez2, Carina Toxqui-Quitl3

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Summary
This summary is machine-generated.

Dedicated hardware significantly accelerates microscopy autofocus algorithms. Implementing autofocus functions on multicore CPUs and GPUs reduced computation time by up to 23x, enabling real-time applications.

Keywords:
Hyper-Qautofocus functionsgraphic processing unitmicroscopy imagesmulticore central processing unitnested parallelismparallel computingtuberculosis dataset

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Area of Science:

  • Microscopy
  • Computational Imaging
  • Hardware Acceleration

Background:

  • Autofocus algorithms in microscopy rely on image sharpness analysis.
  • These autofocus functions (AFs) are computationally intensive due to multi-image processing.

Purpose of the Study:

  • To evaluate the performance of dedicated hardware for speeding up autofocus processes.
  • To compare the implementation of autofocus algorithms on multicore CPU and GPU architectures.

Main Methods:

  • Implemented four autofocus algorithms on multicore CPU and GPU platforms.
  • Conducted experiments using 300 image stacks containing tuberculosis bacilli.

Main Results:

  • Achieved acceleration of autofocus computation time by up to 23 times compared to serial processing.
  • Demonstrated significant speed-up for specific autofocus algorithms on parallel hardware.

Conclusions:

  • Optimal utilization of multicore CPUs and GPUs effectively accelerates autofocus.
  • Hardware acceleration is viable for real-time microscopy autofocus applications.