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首页> 外文期刊>Digital Signal Processing >Suppression of impulse noise in MR images using artificial intelligent based neuro-fuzzy adaptive median filter
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Suppression of impulse noise in MR images using artificial intelligent based neuro-fuzzy adaptive median filter

机译：基于人工智能的神经模糊自适应中值滤波器抑制MR图像中的脉冲噪声

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This paper presents a new artificial intelligent based neuro-fuzzy rule base adaptive median filter for removing highly impulse noise. Since the filter is rule base, it is called neuro-fuzzy rule base adaptive median (NFRBAM) filter. The NFRBAM filter is an improved version of switch mode fuzzy adaptive median filter (SMFAMF) and is presented for the purpose of noise reduction of images corrupted with additive impulse noise. The NFRBAM filter consists of a decision unit and three different types of filters. In the decision unit, the noisy input image is directed to the proper filter with respect to the noise density. Neuro-fuzzy rule based approach is used in both decision and filtering parts. In artificial neural network, multi layer perceptron (MLP) architecture with backpropagation (BP) algorithm is used for noise detection and removing highly impulse noise corrupted MR images. In fuzzy logic, bell-shaped membership function is employed in order to obtain better results. Experimental results indicate that the proposed filter is improvable with the increased fuzzy rules to reduce more noise corrupted images and preserve image details more than SMFAMF. (c) 2007 Elsevier Inc. All rights reserved.
机译：本文提出了一种新的基于人工智能的神经模糊规则库自适应中值滤波器，用于去除高脉冲噪声。由于该过滤器是基于规则的过滤器，因此称为神经模糊规则库自适应中值（NFRBAM）过滤器。 NFRBAM滤波器是开关模式模糊自适应中值滤波器（SMFAMF）的改进版本，旨在降低因加性脉冲噪声而损坏的图像的噪声。 NFRBAM过滤器由决策单元和三种不同类型的过滤器组成。在判定单元中，关于噪声密度，将有噪声的输入图像定向到适当的滤波器。基于神经模糊规则的方法既用于决策部分，又用于过滤部分。在人工神经网络中，带有反向传播（BP）算法的多层感知器（MLP）体系结构用于噪声检测和去除高脉冲噪声破坏的MR图像。在模糊逻辑中，采用钟形隶属函数以获得更好的结果。实验结果表明，与SMFAMF相比，所提出的滤波器可通过增加模糊规则来改进，以减少更多的噪声破坏图像并保留图像细节。 （c）2007 Elsevier Inc.保留所有权利。

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