利用改进的bpann中值滤波技术去除高密度的椒盐噪声

Bhat Jasra, Aniqa Yaqoob, S. Dubey
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引用次数: 9

摘要

本文提出了一种基于反向传播神经网络和自适应中值滤波的椒盐噪声去除方法。该方法利用反向传播神经网络的监督学习能力在第一阶段去除椒盐噪声,在第二阶段使用自适应中值滤波增强图像质量。它克服了传统中值滤波的缺点,保留了细节。实验结果表明,该算法优于基于神经网络的模型和其他传统的滤波机制。即使对于高密度噪声图像,性能也非常好。
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Removal of high density salt and pepper noise using BPANN-modified median filter technique
In this paper an efficient yet simple approach of salt and pepper noise removal based on back propagation neural network and adaptive median filtering has been suggested. The proposed method uses supervised learning capability of back-propagation neural network to remove the salt and pepper noise in first phase and adaptive median filter is used to enhance the image quality in second phase. It overcomes all drawbacks of conventional median filtering by preserving the fine details. Experimental results show that the algorithm performs better than neural network based model & other conventional filtering mechanisms. Performance is exceptionally good even for high density noised images.
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