一种梯度阈值自动调焦算法

传统的梯度自动调焦算法计算量大，抗噪声能力弱，影响调焦的实时性及调焦曲线的单峰性和灵敏度，因此提出一种梯度阈值自动调焦算法，提高调焦性能，满足光电跟踪系统能实时、准确地进行自动调焦的要求。该算法先以图像的局部方差作为局部阈值区分边缘像素与非边缘像素，再计算整幅图像的一种新的标准差作为全局阈值来削弱噪声和背景的影响，最后对阈值预处理后的图像采用梯度调焦算法计算其调焦值，进行清晰度评价。大量实验结果表明，该算法具有实时性好，单峰性强，灵敏度高的特点和良好的抗噪性能。该算法用于光电跟踪系统的自动调焦中时，依然保持上述良好的性能，明显优于传统梯度自动调焦算法。

A gradient threshold auto-focus algorithm

Traditional gradient auto-focus algorithms have large amount of calculation which will cause the reduction of real-time performance. These algorithms are also weak in anti-noise capability which will result in the decline of unimodality and sensitivity. So a gradient threshold auto-focus algorithm was proposed to improve the focusing performance to meet the requirements of real time and accuracy in auto-focusing subsystem of photoelectric tracking system. The proposed algorithm took the local variance as a local threshold to distinguish the edge pixels from non-edge pixels. Then it used a kind of new standard deviation of the whole image as a global threshold to weaken the effects of noise and background. At last, it used one of traditional gradient auto-focus algorithms to calculate the focusing value of the pre-processed image for clarity-evaluation. The results of lots of experiments show that the proposed algorithm has good real- time performance, strong unimodality, high sensitivity and powerful anti-noise capability. When the proposed algorithm is used in the auto-focusing subsystem of photoelectric tracking system, all the attractive performances remain, which traditional gradient auto-focus algorithm can't achieve.