Abstract
Based on the wavefront sensing and propagation, the wavefront-based autofocus approach is proposed for potentially adopted in rapid autofocusing since it can precisely locate the focal plane but only requiring much fewer multi-focal image captures. However, due to the much time spent in serial numerical calculation and analysis, this method fails to improve the autofocusing efficiency as expected. In order to further accelerate the autofocusing speed, we improve this method by adopting the parallel computing in the numerical wavefront propagation and focal analysis procedures based on the graphics processing unit (GPU). Proved by experiments relying on our self-built system, the time consuming for autofocusing can be drastically reduced within 1 s, besides, it can guarantee extremely high focal determination accuracy in various sample cases, indicating that the GPU aided wavefront-based method can be future adopted in commercial microscopes for precise and rapid autofocusing.
Highlights
INTRODUCTIONIn order to avoid the position error and determine the actual focal plane precisely, massive numerically generated multi-focal images are needed, as well as the substantial analysis in focal determination, obviously reducing autofocusing efficiency
Microscopic imaging has developed rapidly in recent years for its distinct advantage of obtaining high quality images of tiny objects, which has found many applications in the fields of biological research and medical diagnosis.[1]
The time consuming in wavefront propagation and focal analysis can be drastically reduced in comparisons with the previously used serial computing process implemented on the central processing unit (CPU), the graphics processing unit (GPU) aided wavefront-based autofocusing method can be considered as a potential tool for rapid autofocusing in commercial microscopes
Summary
In order to avoid the position error and determine the actual focal plane precisely, massive numerically generated multi-focal images are needed, as well as the substantial analysis in focal determination, obviously reducing autofocusing efficiency. To effectively adopt this wavefront-based autofocus method in microscopy, in this paper, we further improve this method by adopting the parallel computing in the numerical wavefront propagation and focal analysis procedures. The time consuming in wavefront propagation and focal analysis can be drastically reduced in comparisons with the previously used serial computing process implemented on the central processing unit (CPU), the GPU aided wavefront-based autofocusing method can be considered as a potential tool for rapid autofocusing in commercial microscopes
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