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An auto-focus algorithm based on maximum gradient and threshold

机译:基于最大梯度和阈值的自动聚焦算法

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In order to overcome the disadvantages of the traditional auto-focus algorithms which are poor in real-time performance, weak in anti-noise capability and vulnerable to the influence of contrast and background pixels, this paper proposes an auto-focus algorithm based on maximum gradient and threshold. It introduces a threshold factor and takes a new kind of adaptive threshold to remove the pixels contaminated by noise and background in the image, then uses improved Sobel operators to extract maximum gray gradient after image preprocessing and calculates evaluation value. The experimental results show that the proposed algorithm has good real-time performance, strong unimodality, high sensitivity and strong anti-noise capability. In addition, the algorithm is less influenced by contrast and background pixels of the image. It can also control the sensitivity and focusing range of the focusing function. So the proposed algorithm is most suitable for auto-focus subsystem of video monitoring and tracking system.
机译:为了克服传统自动对焦算法实时性差,抗噪能力弱,易受对比度和背景像素影响的缺点,提出了一种基于最大值的自动对焦算法。梯度和阈值。它引入了一个阈值因子,并采用一种新的自适应阈值来去除图像中被噪声和背景污染的像素,然后使用改进的Sobel算子在图像预处理后提取最大灰度梯度并计算评估值。实验结果表明,该算法具有良好的实时性,较强的单峰性,较高的灵敏度和抗噪声能力。另外,该算法受图像对比度和背景像素的影响较小。它还可以控制聚焦功能的灵敏度和聚焦范围。因此,该算法最适合视频监控系统的自动对焦子系统。

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