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首页> 外文会议>International conference on education technology and computer >Denoising Maximum Entropy Image Segmentation Based on Improved Genetic Algorithm
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Denoising Maximum Entropy Image Segmentation Based on Improved Genetic Algorithm

机译：基于改进遗传算法的去噪最大熵图像分割

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摘要

In this paper, we present denoising maximum entropy image segmentation on the basis of maximum histogram entropy and 2D maximum entropy.The approach is divided into two stages: before the segmentation, we first make an image dcspeckle processing, and in the process of seeking the threshold, particle swarm optimization genetic algorithm (PSOGA) is used to reduce computation time and improve solution accuracy by combining the standard genetic algorithm (GA) with particle swarm optimization (PSO).Through segmentation test and comparison, the results obtained by our method are short time-consuming and strong anti-noise ability.Computational results show that PSOGA has good global optimal scarehi capabilities and faster search speed.
机译：在本文中，我们在最大直方图熵和2D最大熵的基础上呈现了去噪了最大熵图像分割。该方法分为两个阶段：在分割之前，我们首先进行图像DCSCeckle处理，并在寻求的过程中进行图像。阈值，粒子群优化遗传算法（PSOGA）用于减少计算时间并通过将标准遗传算法（GA）与粒子群优化（PSO）组合，通过粒子群优化（PSO）.Through分割测试和比较来提高解决方案准确性，通过我们方法获得的结果短暂耗时和强大的抗噪声能力.PSOGA具有良好的全局最佳识别功能和更快的搜索速度。

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