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首页> 外文期刊>International journal of natural computing research >Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method
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Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method

机译:基于亮度保留直方图均衡化和对比度扩展方法的多样性保守混沌人工蜂群算法

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摘要

This study is organized into two parts. The first part introduces two image enhancement methods with the ability to preserve the original brightness of the image. These two methods are: optimal ranged brightness preserved contrast stretching (ORBPCS) method and weighted thresholded histogram equalization (WTHE) method. The efficiency of these two methods crucially depends on the method's associated parameters. To find the optimal values of the parameters Artificial Bee Colony (ABC) algorithm and a novel objective junction have been employed in this study. The second part of this study mainly concentrates on the efficiency increment of ABC algorithm and to develop the proper objective junctions to preserve the original brightness of the image. Some new mechanisms like population diversity measurement technique, use of chaotic sequence etc. are also introduced to enhance the efficiency of traditional ABC algorithm. The objective junctions have been developed by using co-occurrence matrix and peak-signal to noise ratio (PSNR).
机译:本研究分为两个部分。第一部分介绍了两种可以保留图像原始亮度的图像增强方法。这两种方法分别是:最佳距离亮度保留对比度拉伸(ORBPCS)方法和加权阈值直方图均衡(WTHE)方法。这两种方法的效率关键取决于方法的相关参数。为了找到参数的最佳值,人工蜂群算法(ABC)和一种新颖的物镜交界点已被用于这项研究。本研究的第二部分主要集中在ABC算法的效率增量上,并开发适当的物镜连接点以保留图像的原始亮度。引入了一些新的机制,如种群多样性测量技术,混沌序列的使用等,以提高传统ABC算法的效率。通过使用共现矩阵和峰值信噪比(PSNR),开发了目标结。

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