Wang Xiaoyi, Yao Tianyang, Liu Mingkang, Zheng Kunlei, Zhao Chengxiang, Xiao Longyuan, Zhu Dongjie
School of Mechatornics Engineering, Henan University of Science and Technology, Luoyang 471003, China.
Henan Key Laboratory of Mechanical Design and Transmission System, Henan University of Science and Technology, Luoyang 471003, China.
Sensors (Basel). 2023 Nov 7;23(22):9017. doi: 10.3390/s23229017.
The clarity evaluation function plays a vital role in the autofocus technique. The accuracy and efficiency of the image clarity evaluation function directly affects the accuracy of autofocus and the speed of focusing. However, classical clarity function values are sensitive to changes in background brightness and changes in object contour length. This paper proposes a normalized absolute values adaptive (NAVA) evaluation function of image clarity. It can eliminate the influence of changes in background brightness and the length of the measured object contour on the image clarity function value. To verify the effectiveness of the NAVA function, several experiments were conducted under conditions of virtual master gear images and actual captured images. For actual captured images, the variation of the evaluation results of the NAVA function is far less than the corresponding variation of the classic clarity function. Compared with classical clarity evaluation functions, the NAVA function can provide normalized absolute clarity values. The correlations between the NAVA function results of image clarity and both the contour length and background brightness of the tested object are weak. The use of the NAVA function in automatic and manual focusing systems can further improve focusing efficiency.
清晰度评估函数在自动对焦技术中起着至关重要的作用。图像清晰度评估函数的准确性和效率直接影响自动对焦的精度和对焦速度。然而，经典清晰度函数值对背景亮度变化和物体轮廓长度变化敏感。本文提出了一种图像清晰度的归一化绝对值自适应（NAVA）评估函数。它可以消除背景亮度变化和被测物体轮廓长度对图像清晰度函数值的影响。为验证NAVA函数的有效性，在虚拟标准齿轮图像和实际采集图像条件下进行了多项实验。对于实际采集的图像，NAVA函数评估结果的变化远小于经典清晰度函数的相应变化。与经典清晰度评估函数相比，NAVA函数可以提供归一化的绝对清晰度值。图像清晰度的NAVA函数结果与被测物体的轮廓长度和背景亮度之间的相关性较弱。在自动和手动对焦系统中使用NAVA函数可以进一步提高对焦效率。
