A New Fuzzy Rule Based Contrast Enhancement Method using The Two-Steps Automatic Clustering Algorithm

Ha Che-Ngoc, A. Pham-Chau, Dibya Jyoti Bora
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引用次数: 4

Abstract

The contrast is a major factor influencing the image quality; therefore, image contrast enhancement technique is more and more widely applied in the field of image processing. In this paper, a new fuzzy rule-based contrast enhancement method using the two-steps automatic clustering algorithm is proposed. Specifically, based on the Automatic clustering algorithm, a state-of-art method in cluster analysis and data mining, this paper proposes a two-steps Automatic clustering method to determine the number of fuzzy sets and locate the critical point in membership functions so that they are suitable for the distribution of pixel intensity values. The experiments on the "Lena" image and other natural images demonstrate that the new method can effectively enhance the contrast of the images and meet the demands of human eyes perception at the same time.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
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一种基于模糊规则的两步自动聚类对比度增强方法
对比度是影响图像质量的主要因素;因此,图像对比度增强技术在图像处理领域得到了越来越广泛的应用。本文提出了一种新的基于模糊规则的对比度增强方法,该方法采用两步自动聚类算法。具体而言,本文在聚类分析和数据挖掘领域的最新方法自动聚类算法的基础上,提出了一种两步自动聚类方法,以确定模糊集的数量,并确定隶属函数中的临界点,使其适合像素强度值的分布。在“Lena”图像和其他自然图像上的实验表明,新方法可以有效地增强图像的对比度,同时满足人眼感知的要求。这是一篇在知识共享署名许可(http://creativecommons.org/licenses/by/4.0/)条款下发布的开放获取文章,该许可允许在任何媒介上不受限制地使用、分发和复制,只要原始作品被适当引用。
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