改进萤火虫优化算法的Renyi熵污油图像分割

Renyi entropy based on improved firefly optimization algorithm for image segmentation of waste oil

针对传统Renyi熵方法在分割污油图像时存在图片差距大、无法根据不同图片进行最优分割的问题,提出改进萤火虫算法对二维Renyi熵分割算法中的α值进行寻优来解决上述问题。分析了采集的污油图片特点以及对污油图片进行分割的必要性;针对多目标寻优精度不高和后期收敛速度较慢的问题,对萤火虫算法进行了改进,并对初始萤火虫位置进行混沌优化处理,使结果达到全局最优;利用基于改进萤火虫算法的Renyi熵图像分割算法对采集的污油图片进行阈值分割实验,并与二维Renyi熵分割、粒子群算法(PSO)Renyi熵分割方法进行比较。实验结果表明:本文提出的算法可以有效地对污油区域进行分割,能够快速地实现复杂图像的精确处理。

Aiming at the problem that the traditional Renyi entropy method has large image gaps and cannot be optimized according to different images when dividing dirty oil images,an improved firefly algorithm is proposed to solve the above problem by optimizing the alpha value of two-dimensional Renyi entropy segmentation algorithm.First,we analyze the characteristics of an acquired oil image and the necessity of segmenting a dirty oil picture;second,aiming at the problems of low optimization precision and slow convergence speed in the later stage,the firefly algorithm is improved to make the initial position of the firefly chaos optimization processing results reach the global optimum,and then Renyi entropy image segmentation algorithm based on the improvement of the firefly algorithm is applied to the experiments of threshold value segmentation of the waste oil image.Finally,the algorithm proposed in this paper is used to collect oil image segmentation in experiments,and the results are compared with the 2 D Renyi entropy segmentation and the particle swarm optimization(PSO)Renyi entropy segmentation method.The experimental results illustrate that the proposed algorithm can effectively segment the waste oil area and quickly achieve accurate processing of complex images.