The positive and random impulse noise reduction using ann and Gaussian recursive filter

Mehrab Ghanat Bari, Fatemeh Ghanat Bari, Jianqiu Zhang
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引用次数: 3

Abstract

In this paper, a new method of filtering is introduced which can be adopted to neural networks, which can be applied to improve the corrupted images by salt-pepper noises. In the first step, neural networks are used to identify the location of the noises in image and in the next step; the identified noisy pixel will be reduced by using Gaussian recursive filter. This method is called the Neural Network Gaussian (NNG) filter. Using neural networks to recognize the location of salt-pepper noises prevents incorrect recognition of noise and increase the quality of noise reduction process. Moreover, by using Gaussian recursive filters against the typical median filter, which used only to omit trivial noises, the algorithm performance will also be improved significantly.
采用人工神经网络和高斯递归滤波器对正随机脉冲噪声进行降噪
本文介绍了一种适用于神经网络的滤波方法,可用于改善椒盐噪声对图像的破坏。首先,利用神经网络识别图像中噪声的位置;利用高斯递归滤波器对识别出的噪声像素进行降噪。这种方法被称为神经网络高斯(NNG)滤波器。利用神经网络识别椒盐噪声的位置,防止了对噪声的错误识别,提高了降噪过程的质量。此外,通过使用高斯递归滤波器来对抗典型的中值滤波器,该算法的性能也将得到显著提高。
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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