摘要
清晰度评价函数是自动聚焦技术的核心部分,性能良好的聚焦曲线应该具有单峰性、无偏性、高灵敏度和抗噪性。通过将梯度差分与统计相关结合使用,提出了一种用邻域互相关对每个像素的梯度值进行加权的算法,并设定阈值去除贡献小的像素点。实验中使用定量指标对所提算法、一些传统算法以及一种梯度阈值算法的性能进行了评估。结果表明,所提算法在灵敏度和抗噪性方面效果较优。
The definition evaluation function is the core part of automatic focusing technique, and the focusing curve which has better performance must be unimodality, unbiasedness, high sensitivity and anti-noise. A new function is pro- posed based on the combination of the gradient difference and the statistical correlation. The gradient value of each pixel is weighted by the neighbourhood cross-correlation, and the threshold is set to remove these pixels whose contributions aresmall. The experiment uses some quantitative indexes to evaluate the performances of the proposed algorithm, some tra- ditional algorithms and a gradient threshold algorithm. The result shows that the proposed algorithm has better sensitivi- ty and anti-noise.
出处
《光学技术》
CAS
CSCD
北大核心
2016年第4期329-332,共4页
Optical Technique
基金
国家自然科学基金(61201370)
关键词
图像处理
自动聚焦
清晰度评价函数
梯度
互相关
image processing
automatic focusing
definition evaluation function
gradient
cross-correlation
