Difference auto-focusing based on particle swarm optimization searching and wavelet evaluation
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摘要:为进一步提高图像法自动聚焦的性能,提出了一种差分式提取图像边缘的方法,并构造了图像清晰度的小波评价函数,同时利用微粒群(PSO)算法对聚焦区域进行快速搜索。首先,介绍了差分式边缘提取方法及其优势,给出了一种评价区域的选取判据以及基于PSO的高效搜索方法;然后,对小波评价函数参数进行了比较分析和优选;最后,与传统方法进行了对比实验。结果表明,由于采用了差分式提取方法以及新的自适应聚焦窗口和评价函数,聚焦曲线较传统方法具有更高的调焦分辨率,PSO算法的使用使聚焦速度提高了约170 ms,聚焦精度约为2.3 m,同时调焦效果不受初始位置的影响。Abstract:In order to enhance the auto-focusing capability of a imaging system, a difference image extraction method is proposed. Meanwhile, a evaluation function based on wavelets and a region selecting technique using Particle Swarm Optimization(PSO) are given. The principle and superiority of the difference image extraction method are introduced. Then a selecting criterion and a searching method using PSO for region evaluation are described, and the parameters for wavelet based evaluation function are compared and optimized. Finally, emulation experiments are practiced to analyze the advantages and disadvantages of traditional and proposed methods. Results indicate that the proposed method can offer a higher resolution to the focusing curve due to the difference image extraction, new focusing window and focus evaluation. Moreover, it has improved the focusing speed by 170 ms and provided the focusing precision about 2.3 m. Furthermore, its focusing effects are always satisfactory with different initial positions.
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