基于遗传算法的图像分割处理技术研究

Research on image segmentation technology based on genetic algorithms

旨在使用遗传算法对带有底部噪声的图像进行处理,并通过对遗传算法的改进实现处理效果的提升。结合图像分割阐述了遗传算法的工作机制,分析了适应度计算、选择、交叉和变异等主要模块的设计方法,阐明了代沟与优秀个体的关系、不同代间的个体替换关系、交叉点的选取方法与变异位置的选择、种群数量的保持等关键性问题,并给出了参数设置的具体值。使用该算法对带底部噪声的图片进行了处理,结果表明传统遗传算法可以将目标图像从存在噪声的背景中分离出来,但处理时间为7.416s。为提高处理效率,利用进化代数和个体的适应度值自适应地调整种群的交叉概率和变异概率对传统算法进行了改进。使用改进的遗传算法对同一噪声图像进行了分割处理,结果表明改进后的遗传算法图像分割效果更佳,处理时间仅为0.751s,效率提高了近10倍。

The purpose of this paper is to use genetic algorithm(GA) to process image with bottom noise, and the processing effect is improved through the improvement of GA. Combining with image segmentation, this paper expounds the working mechanism of GA and the design methods of main modules such as fitness calculation, selection, crossover and mutation, and gives the specific values of parameter setting. The key issues such as the relationship between generation gap and excellent individuals, the substitution relationship between individual among different generations, the selection method of intersection points and the selection of mutation positions, and the maintenance of population number are clarified. The image with bottom noise is processed by this algorithm, the results show that the target image can be separated from the background with noise by GA, but the processing time is 7. 416 seconds. In order to improve the processing efficiency, the traditional algorithm is improved by adaptively adjusting the crossover probability and mutation probability of the population using evolutionary generation and individual fitness values. The same noise image is segmented by improved GA. The results show that the improved GA has better image segmentation effect, the processing time is only 0. 751 seconds, the efficiency is improved by nearly 10 times.