基于人工蜂群算法优化的直觉模糊集局部对比度增强

ComTech Pub Date : 2017-03-31 DOI:10.21512/COMTECH.V8I1.3777
D. M. Wonohadidjojo
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引用次数: 0

摘要

本文介绍了细胞图像的增强方法。在局部对比度增强中使用的第一种方法是直觉模糊集(IFS)。所提出的方法是利用人工蜂群(ABC)算法优化的IFS。采用ABC法对IFS的隶属函数参数进行了优化。为了测量图像质量,应用了图像增强度量(IEM)。将使用这两种方法的局部对比度增强的结果与使用直方图均衡方法的结果进行比较。使用两张MDCK细胞图像进行测试。通过观察增强图像和IEM值来评估使用这两种方法的局部对比度增强的结果。结果表明,该方法优于直方图均衡方法。此外,使用IFSABC的方法比IFS方法更好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Local Contrast Enhancement Using Intuitionistic Fuzzy Sets Optimized By Artificial Bee Colony Algorithm
The article presented the enhancement method of cells images. The first method used in the local contrast enhancement was Intuitionistic Fuzzy Sets (IFS). The proposed method is the IFS optimized by Artificial Bee Colony (ABC) algorithm. The ABC was used to optimize the membership function parameter of IFS. To measure the image quality, Image Enhancement Metric (IEM) was applied. The results of local contrast enhancement using both methods were compared with the results using histogram equalization method. The tests were conducted using two MDCK cell images. The results of local contrast enhancement using both methods were evaluated by observing the enhanced images and IEM values. The results show that the methods outperform the histogram equalization method. Furthermore, the method using IFSABC is better than the IFS method.
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