Auto-focusing and control of micro-vision system

The auto-focusing evaluation function and a controller were researched based on an automated microscopy. At first, the Discrete Wavelet Transform (DWT) and Sobel-Tenengrad function were introduced, and a new auto-focusing evaluation function was proposed by combining the DWT with the Sobel-Tenengrad operator. Then, the defocused and focused sample pictures were used to train the Self-organizing Map (SOM) algorithm in a unsupervised method, and the Particle Swarm Optimization (PSO) was used to accelerate the training process. Finally, an auto-focusing experiment was carried out by using the trained SOM controller. The experimental results show that the new auto-focusing function has the characters of single steep peak and strong robustness to different samples and objective lenses. The results also indicate that the SOM based controller only takes 7.6 steps for auto-focusing process on average, and the focusing speed and stability have been greatly improved compared with that using the mountain climbing method. Moreover, it processes or recognizes the input image only for about 120 ms. The proposed method has met the requirements of auto-focusing of micro-vision system, and obtained good results.