基于粒子群优化的自适应灰度化算法

The Self-Adaptive Grayscale Algorithm Based on Particle Swarm Optimization

为了解决传统图像灰度化方法难以充分保存初始图像的原有特征的缺点,提出了一种基于粒子群优化的自适应灰度化算法.根据高像素值像素在彩色图像单通道的灰度直方图分布,通过粒子群寻优选取最好的阈值并自动生成的各分量权值进行图像灰度化.仿真实验表明,比起传统灰度化方法,提出的方法避免了在灰度化过程中,面对不同的彩色图像而通道权值一成不变的缺陷,更好地保留了初始彩色图像的原有特征,为后续的图像处理提供了良好的基础.

To overcome the shortcomings of traditional image graying methods that can hardly preserve the original features of the original image, an adaptive gray-level algorithm based on particle swarm optimization is presented. According to the gray histogram distribution of the high pixel in the single channel of the color image, the best threshold is selected by the particle swarm optimization and the weight values of each component are automatically generated to grayscale the image. Simulation experiments prove that compared with the traditional grayscale method, the method proposed in this paper avoids the defect that the weight of the channel is unchangeable in the face of different color images in the process of grayscale. The original feature of the initial color image is preserved better, which provides a good basis for the subsequent image processing.