基于蜂群算法的多小波图像去噪研究

Multi-wavelet image denoising based on artificial bee colony algorithm

针对在多小波图像去噪中阈值难以选取问题,提出基于群体智能算法—人工蜂群算法(artificial bee colonyalgorithm,ABC)优化多小波阈值。详细介绍了群体智能算法的发展历程和分类,阐述了ABC算法的基本原理、工作流程,及其优化多小波阈值在图像去噪中的具体步骤,比较了遗传算法(genetic algorithm,GA)、粒子群算法(par-ticle swarm optimization,PSO)、蚁群算法(antcolonyoptimization,ACO)以及ABC算法4种算法各自的优缺点。将提出的方法与GA算法和PSO算法优化多小波阈值进行了对比,通过仿真,证明提出的算法可以有效地去除高斯白噪声,提高图像的峰值信噪比(peak signal to noise ratio,PSNR),具有很好的去噪效果。

Aiming at the difficult problem of selecting the multi-wavelet image denoising threshold,we proposed the optimize multi-wavelet threshold algorithm based on the swarm intelligence—artificial bee colony algorithm（ABC）.The method is widely used in image processing.This paper describs the course of development of swarm intelligence algorithms and classification,after elaborating on the basic principle of artificial bee colony algorithm workflow,and the details of the artificial bee colony algorithm to optimize the multi-wavelet threshold in image denoising concrete steps.We also elaborated the respective advantages and disadvantages of the four algorithms of the genetic algorithm,particle swarm optimization,ant colony algorithm and artificial bee colony algorithm.Compared with the genetic algorithm and particle swarm algorithm optimized the multi-wavelet threshold,through experimental simulation,the proposed algorithm effectively removed Gaussian white noise,improved the image of the peak signal-to-noise ratio,and had a good denoising effect.