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Subpixel displacement measurement method based on the combination of particle swarm optimization and gradient algorithm

机译:基于粒子群优化与梯度算法相结合的亚像素位移测量方法

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摘要

A subpixel displacement measurement method based on the combination of particle swarm optimization (PSO) and gradient algorithm (GA) was proposed for accuracy and speed optimization in GA, which is a subpixel displacement measurement method better applied in engineering practice. An initial integer-pixel value was obtained according to the global searching ability of PSO, and then gradient operators were adopted for a subpixel displacement search. A comparison was made between this method and GA by simulated speckle images and rigid-body displacement in metal specimens. The results showed that the computational accuracy of the combination of PSO and GA method reached 0.1 pixel in the simulated speckle images, or even 0.01 pixels in the metal specimen. Also, computational efficiency and the antinoise performance of the improved method were markedly enhanced.
机译:为了提高遗传算法的精度和速度,提出了一种基于粒子群算法(PSO)和梯度算法(GA)相结合的亚像素位移测量方法,该方法是一种较好的工程应用方法。根据PSO的全局搜索能力,获得了初始整数像素值,然后将梯度算子用于子像素位移搜索。通过模拟散斑图像和金属样品中的刚体位移,将该方法与遗传算法进行了比较。结果表明,PSO和GA相结合的计算精度在散斑图像中达到了0.1个像素,在金属试样中甚至达到了0.01个像素。而且,改进方法的计算效率和抗噪性能也得到了显着提高。

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