基于改进 FA 算法与不完全 Beta 函数的图像增强技术

针对传统计算机在复杂图像信息分析以及后期处理不达预期的问题,提出了利用改进原始的萤火虫算法 (FA)在不完全 Beta 函数上动态寻优调整灰度曲线的光电图像增强新策略。 新策略主要从算法角度出发改进传统 FA 算法,针对原有的吸引度容易造成局部最优等问题引入新吸引度公式、针对算法陷入局部震荡添加自扰动和克 服陷入局部最优的迭代检测环节,改进完成的新算法(Firefly Algorithm Growth,FAG)结合非完全 Beta 函数动态寻 找最优值下的图像灰度曲线。 将改进的 FAG 与 FA 新老算法在四种常见基准函数上进行对比实验测试他们的性 能,结果显示改良 FAG 算法在性能上更优;在改良 FAG 结合非完全 Beta 与 FA 结合非完全 Beta 增强同一图像的 实验中加入直方图算法增强图像作为对照组,综合结果显示改进后的新策略更胜一筹。 综合结果显示群智能算法 在结合图像处理手段来达到图像增强的目的上具有很好的应用价值,新策略在低对比度条件下的光电图像实现了 有效的增强。

Image Enhancement Technology Based on Improved FA Algorithm and Incomplete Beta Function

Aiming at the problem that the traditional computer cannot meet the expectation in the information analysis and post- processing of complex images, a new photoelectric image enhancement strategy was proposed, which used the improved original firefly algorithm ( FA) to dynamically optimize and adjust the gray curve on the incomplete Beta function. The new strategy mainly improved the traditional FA algorithm from the perspective of the algorithm, introduced a new attraction formula for the original attraction degree that was prone to local optimality, added self-perturbation to solve the problem that the algorithm fell into local oscillation, and avoided the iterative detection link that fell into local optimality. The improved new Firefly Algorithm Growth ( FAG) was combined with an incomplete Beta function to dynamically search for image gray curve under optimal value. The improved FAG was compared with the old and new FA algorithms on four common benchmark functions to test their performance, and the results showed that the improved FAG algorithm was superior in terms of performance. In experiments where the same image was enhanced by the improved FAG combined with incomplete Beta and FA combined with incomplete Beta, the histogram algorithm was added to enhance the image as the control group, and the comprehensive results showed that the improved new strategy was superior. The results show that the swarm intelligence algorithm has a good application value in the combination of image processing means to achieve the purpose of image enhancement. The new strategy can effectively enhance photoelectric images under the condition of low contrast.