• Acta Photonica Sinica
  • Vol. 42, Issue 5, 605 (2013)
ZHANG Laixian1、*, SUN Huayan2, GUO Huichao1, and FAN Youchen1
Author Affiliations
  • 1[in Chinese]
  • 2[in Chinese]
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    DOI: 10.3788/gzxb20134205.0605 Cite this Article
    ZHANG Laixian, SUN Huayan, GUO Huichao, FAN Youchen. Auto Focusing Algorithm Based on Largest Gray Gradient Summation[J]. Acta Photonica Sinica, 2013, 42(5): 605 Copy Citation Text show less

    Abstract

    The principle of defocused imaging was introduced based on optical system. The defects of classical auto focusing algorithms based on gray gradient were presented, which were not considering all the gray gradient and affected by noise easily. According to the defects of classical algorithms, a new auto focusing algorithm based on largest gray gradient summation was proposed, which was realized through adding the product of the maximum and the difference of the maximum and the minimum of the eight neighborhood gray gradient of each pixel in the selected auto focusing region. The principle of restraining noise of the new algorithm was discussed. The principle and progress of adaptive variable step climbing algorithm for focusing position searching were introduced. At last, experiments were carried out to test the performance of the new algorithm. Firstly, by calculating the definition value of the original and Gaussian noise added sequences images using new algorithm and classical algorithms, and comparing the results, the advantage of restraining noise of the new algorithm was proved, for using the new algorithm, the definition evaluation of noisy image was calculated better compared to all the other algorithms. Secondly, experiments of auto focusing using different algorithms were carried out, and the results showed that using classical algorithms might focus wrong, while the new algorithm could focus correct and rapidly, which was an obvious advantage comparing to classical algorithms.
    ZHANG Laixian, SUN Huayan, GUO Huichao, FAN Youchen. Auto Focusing Algorithm Based on Largest Gray Gradient Summation[J]. Acta Photonica Sinica, 2013, 42(5): 605
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