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letter
. 2020 Jun 19;20(12):3479. doi: 10.3390/s20123479

Table 1.

Comparison of prior autofocusing system studies.

Development Team Key Points Compared with Our Proposed System Reference
Chen et al.

  • Image-based

  • Utilizing SOM neural network to calculate the individual FV of the image spatial frequency function

  • Utilizing the DWT method for sharpness measuring

 Uses a complex algorithm [7]
Jeon et al.

  • Fully digital autofocusing

  • Fast and precise autofocusing

  • Cooperation with point spread function

 Has a high-cost image capturing system [31]
Koh et al.

  • Adopting two low-pass filters and double apertures

  • Position variance of gradient magnitude (VGM)

 Requires a complex algorithm to deal with blurred images [32]
Yamada et al.

  • High-power zoom lens

  • Optical difference by utilizing a switch prism

 Requires a high-cost and precise positioning for image capturing [33]
Wang et al.

  • Response intensity of dual near-focusing position

 Without retrieving the information of moving direction near the focal plane [41]