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首页> 外文期刊>Complexity >Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm
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Otsu’s Image Segmentation Algorithm with Memory-Based Fruit Fly Optimization Algorithm

机译：otSu的基于存储器的果蝇优化算法的图像分割算法

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In this paper, the most common pepper noise in grayscale image noise is investigated in depth in the median filtering algorithm, and the improved median filtering algorithm, adaptive switching median filtering algorithm, and adaptive polar median filtering algorithm are applied to the OTSU algorithm. Two improved OTSU algorithms such as the adaptive switched median filter-based OTSU algorithm and the polar adaptive median filter-based OTSU algorithm are obtained. The experimental results show that the algorithm can better cope with grayscale images contaminated by pretzel noise, and the segmented grayscale images are not only clear but also can better retain the detailed features of grayscale images. A genetic algorithm is a kind of search algorithm with high adaptive, fast operation speed, and good global space finding ability, and it will have a good effect when applied to the threshold finding of the OTSU algorithm. However, the traditional genetic algorithm will fall into the local optimal solution in different degrees when finding the optimal threshold. The advantages of the two interpolation methods proposed in this paper are that one is the edge grayscale image interpolation algorithm using OTSU threshold adaptive segmentation and the other is the edge grayscale image interpolation algorithm using local adaptive threshold segmentation, which can accurately divide the grayscale image region according to the characteristics of different grayscale images and effectively improve the loss of grayscale image edge detail information and jagged blur caused by the classical interpolation algorithm. The visual effect of grayscale images is enhanced by selecting grayscale images from the standard grayscale image test set and interpolating them with bilinear interpolation, bucolic interpolation, NEDI interpolation, and FEOI interpolation for interpolation simulation validation. The subjective evaluation and objective evaluation, as well as the running time, are compared, respectively, showing that the method of this paper can effectively improve the quality of grayscale image interpolation.
机译：在本文中，在灰度级图像噪声的最常见椒盐噪声在深度调查了中值滤波算法，和改进的中值滤波算法，自适应切换中值滤波算法和自适应极性中值滤波算法被应用到OTSU算法。两种改进OTSU算法，如自适应切换中值获得滤波器基于OTSU算法和极性自适应基于滤波器中值OTSU算法。实验结果表明，该算法能更好地与饼干噪声污染的灰度图像处理，以及分段灰度图像不仅清晰，而且可以更好地保留灰度图像的细节特征。遗传算法是一种具有高适应性，运算速度快和良好的全球空间能力的发现搜索算法，当应用于大津算法的门槛发现它有很好的效果。然而，找到最佳阈值时的传统的遗传算法将落入在不同程度的局部最优解。在本文提出的两个插值方法的优点是，一个是使用OTSU阈自适应分段和所述之外的其它边缘的灰度图像插值算法是使用局部自适应阈值分割的边缘的灰度图像插值算法，它可以准确地分割灰度图像区域根据不同的灰度级图像的特性，有效地提高的灰度级图像的边缘细节的信息和所造成的经典插值算法锯齿状模糊的损失。灰度图像的视觉效果，通过从标准灰度图像测试集合中选择的灰度图像，并用双线性内插，内插田园，NEDI插值和FEOI插值内插仿真验证插值它们增强。主观评价和客观的评价，以及在运行时间，被分别进行比较，示出了本文所述的方法可有效地提高灰度图像插值的质量。

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