一种基于模糊理论的图像增强改进方法

Weidong Qu, Ming Shao, Xiang-zheng Cheng, Yunfeng Zhang, Wei Liu
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引用次数: 0

摘要

人工智能及其应用不仅在控制领域,而且在信号和信息处理领域都得到了爆炸性的发展。模糊理论是人工智能的一个重要分支。在图像处理的模糊增强理论中,常采用Pal函数作为隶属函数。虽然该函数具有较好的滤波效果,但其模糊因子往往为经验值,导致输入图像不同时图像增强效果不同,增强图像的细节不清晰,往往会出现较差的增强效果。本文将模糊因素作为变量来考虑。同时,构建了评价函数对增强效果进行评价,并采用合适的优化算法自动获得模糊因子的最优值。仿真结果表明了改进方法的良好性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An improved method of image enhancement based on fuzzy theory
Artificial intelligence (AI) and its application are developed explosively not only in control field but also in signal and information processing field. Fuzzy theory is an important branch of AI. In fuzzy enhancement theory of image processing, Pal function is often employed as the membership function. Although this function possesses good filtering effect, the fuzzy factors of the function are often empirical values, which results to different image enhancement effects when the input images are different, and details of the enhancement image are not clear, then bad enhancement effect always appears. In this paper, the fuzzy factors are considered as variables. At the same time, an evaluation function is constructed to evaluate the enhancement performance, and a suitable optimization algorithm is used to obtain the most optimum values of the fuzzy factors automatically. Simulation results show good performance of the improved method.
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