基于亮度保持直方图均衡化和对比度拉伸方法的多样性保持混沌人工蜂群算法

K. G. Dhal, Sanjoy Das
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引用次数: 16

摘要

本研究分为两部分。第一部分介绍了两种能够保持图像原有亮度的图像增强方法。这两种方法分别是:最优范围亮度保持对比度拉伸ORBPCS法和加权阈值直方图均衡化WTHE法。这两种方法的效率主要取决于方法的相关参数。为了求出参数的最优值,本文采用了人工蜂群ABC算法和一种新的目标函数。本研究的第二部分主要集中在ABC算法的效率增量和开发合适的目标函数来保持图像的原始亮度。为了提高传统ABC算法的效率,引入了种群多样性测量技术、混沌序列的使用等新的机制。利用共现矩阵和峰值信噪比建立了目标函数。
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Diversity Conserved Chaotic Artificial Bee Colony Algorithm based Brightness Preserved Histogram Equalization and Contrast Stretching Method
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 function 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 functions 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 functions have been developed by using co-occurrence matrix and peak-signal to noise ratio PSNR.
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