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Design of image intelligent focusing system based on improved SMD function and RBF algorithm.

Qianwei Deng1,2, Chee-Onn Wong2, Roopesh Sitharan2

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This study introduces an improved focus model using digital image processing and enhanced data fusion for optical sensors. The new sum-modulus difference method significantly boosts real-time perception accuracy and dynamic control precision.

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Area of Science:

  • Digital image processing
  • Optical sensor technology
  • Data fusion methodologies

Background:

  • Traditional focus models in optical sensors suffer from inadequate accuracy.
  • Limitations in real-time perception and dynamic control hinder advanced applications.
  • Challenges in integrating computational and physical processes in open environments.

Purpose of the Study:

  • To overcome the accuracy limitations of traditional focus models.
  • To enhance the precision of real-time perception and dynamic control.
  • To facilitate seamless interaction and integration in open computational-physical environments.

Main Methods:

  • Developed an enhanced sum-modulus difference (SMD) evaluation function based on threshold value evaluation.
  • Utilized threshold segmentation to compute gray values and identify optimal image segmentation thresholds.
  • Applied the identified threshold to a focus search strategy using the radial basis function (RBF) algorithm.
  • Implemented an intelligent focusing system on the Zynq development platform.

Main Results:

  • The improved SMD evaluation function rapidly identifies the peak point of the gray variance curve.
  • The method accurately ascertains the optimal focal plane position.
  • Demonstrated notable enhancement in the sensitivity of the focusing model.

Conclusions:

  • The proposed enhanced SMD function and RBF-based focus strategy significantly improve focusing accuracy and sensitivity.
  • The intelligent focusing system on the Zynq platform offers a robust solution for real-time perception.
  • This research contributes to more precise and integrated computational-physical systems.