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Impulse noise reduction using hybrid neuro-fuzzy filter with improved firefly algorithm from X-ray bio-images
International Journal of Imaging Systems and Technology ( IF 3.3 ) Pub Date : 2020-06-21 , DOI: 10.1002/ima.22453
R. Pugalenthi 1 , A. Sheryl Oliver 1 , M. Anuradha 1
Affiliation  

Noise filtering performance in medical images is improved using a neuro‐fuzy network developed with the combination of a post processor and two neuro‐fuzzy (NF) filters. By the fact, the Sugeno‐type is found to be less accurate during impulse noise reduction process. In this paper, we propose an improved firefly algorithm based hybrid neuro‐fuzzy filter in both the NF filters to improve noise reduction performance. The proposed noise reduction system combines the advantages of the neural, fuzzy and firefly algorithms. In addition, an improved version of firefly algorithm called searching diversity based particle swarm firefly algorithm is used to reduce the local trapping problem as well as to determine the optimal shape of membership function in fuzzy system. Experimental results show that the proposed filter has proved its effectiveness on reducing the impulse noise in medical images against different impulse noise density levels.

中文翻译:

使用混合神经模糊滤波器和改进的 X 射线生物图像萤火虫算法减少脉冲噪声

使用由后处理器和两个神经模糊 (NF) 滤波器组合开发的神经模糊网络,可以提高医学图像中的噪声过滤性能。事实上,发现 Sugeno 型在脉冲降噪过程中不太准确。在本文中,我们在两个 NF 滤波器中提出了一种改进的基于萤火虫算法的混合神经模糊滤波器,以提高降噪性能。所提出的降噪系统结合了神经、模糊和萤火虫算法的优点。此外,改进版本的萤火虫算法称为基于搜索多样性的粒子群萤火虫算法,用于减少局部陷阱问题以及确定模糊系统中隶属函数的最佳形状。
更新日期:2020-06-21
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