Automatic Focusing Algorithm Based on Fully Automatic Control Microscope

  • Zhenlong Hu
Keywords: Fully-Automatically Controlled Microscope, Autofocus, Self-Organizing Map (SOM) Neural Network, Evaluation Function


The autofocus level of the microscope is one of the factors that affect the user's work efficiency. In this paper, in
order to improve the efficiency of the search algorithm while ensuring the search accuracy, a focus search
algorithm with the function of predicting the best focus position based on the SOM neural network is proposed.
The search algorithm makes full use of the automatic clustering function of SOM neural network. First complete
the training of the SOM neural network through a large number of samples. The trained SOM neural network
can directly predict the best focus position of the lens based on the input sample data. The results show that the
auto-focusing algorithm proposed in this paper has high stability, and the probability of successful auto-focusing
is not less than 94%. In terms of clarity threshold selection, it should be based on the number of cells. As for the
clarity curve, the curve of the energy spectrum method has obvious peaks and high sensitivity. In terms of the
real-time nature of autofocus, the motor is a key factor restricting the focusing speed.