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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最大熵图像去噪

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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：本文在最大直方图熵和二维最大熵的基础上提出了最大熵图像降噪方法。该方法分为两个阶段：在分割之前，首先进行图像dc散斑处理，然后在寻找图像的过程中对图像进行散斑处理。通过将标准遗传算法（GA）与粒子群优化（PSO）相结合，使用阈值粒子群优化遗传算法（PSOGA）减少了计算时间并提高了求解精度。计算结果表明，PSOGA具有良好的全局最优恐吓能力和更快的搜索速度。

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