在聚焦评价算法的研究中, 本文首先对传统的离散余弦变换(DCT)算法和最小核值相似区(SUSAN)算法进行改进, 然后结合改进后的 DCT算法和 SUSAN算法提出一种新的聚焦评价算法, 该算法结合频域评价算法和空间域评价算法各自的优点, 使聚焦曲线在单峰性, 局部极值点和灵敏度等方面与传统算法相比有较大改善。在聚焦窗口的研究中, 本文提出一种基于图像子块重要程度加权的聚焦窗口选择方法, 该方法以总梯度变化率作为图像子块重要程度因子, 将重要程度因子值小于阈值的图像子块视为背景子块, 去除背景子块后剩下的部分为聚焦窗口。新的聚焦窗口选择方法能实现动态的区分目标区域与背景区域。

In the research of the evaluation function, this paper firstly states the improvement Discrete Cosine Transform (DCT) algorithm and Small Univalve Segment Assimilating Nucleus (SUSAN) algorithm. Then, combining with the improved DCT algorithm and SUSAN algorithm, a new focusing evaluation algorithm has been presented. New algorithm collects the respective advantages of frequency domain algorithm and spatial domain algorithm, which enable the focus curve to has a greatly improvement in unimodal, local extreme points and sensitivity. In the research of the focusing window, a new selection method is proposed on the basis of the importance of image sub-block. The new method regards the gradient rate as image sub-block importance factor. Then, put the image sub-block whose factor value is less than threshold as background sub-block and removed. Finally, after removing background sub-block image, treat the rest of part as the focusing window. The new selection method can achieve dynamic distinguishing between target area and background area.