Enhancement of Radiograph Images Based on Chaos Optimization

Yinjie Sun
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引用次数: 1

Abstract

How to select the parameters of high-frequency emphasis filtering (HFEF) in order to enhance radiograph images, this paper presents an enhancing method based on chaos optimization algorithms (COA). Based on the properties of ergodicity, stochastic property and regularity of chaos, the chaos optimization method can get global solution with low computational load. Firstly, an X-ray image is sharpened with HFEF, at the same time, the controllable and improved COA can optimize the parameters so that the optimal clarity of the image may be got, then the contrast of it is enhanced with histogram equalization to visually enhance the medical radiograph images to solve the questions: blurring and dark nature, which radiograph images generally tend to have. Experimental results show the proposed approach is effective and gets competitive visual effects.
基于混沌优化的x线图像增强
本文提出了一种基于混沌优化算法(COA)的高频重点滤波(HFEF)增强方法。基于混沌的遍历性、随机性和规律性,混沌优化方法能以较低的计算量获得全局解。首先利用HFEF对x射线图像进行锐化,同时利用可控改进的COA对参数进行优化,获得最佳的图像清晰度,然后利用直方图均衡化对图像对比度进行增强,从视觉上增强医学x射线图像,解决x射线图像普遍存在的模糊和暗化问题。实验结果表明,该方法是有效的,能获得较好的视觉效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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