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An improved image denoising method based on the pulse coupled neural network

机译:一种基于脉冲耦合神经网络的改进图像去噪方法

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In this paper, we proposed an image denoising filtering method based on PCNN (pulse coupled neural network) for images polluted by the pulse noise. The method firstly determined the position of pixels polluted by noise according to the fire capture features of PCNN. Then, a similar median filtering step was applied in the processing of noised pixels. Finally, the iterations and the sizes of filter window were chosen according to the noise intensity, which realized the adaptive-filtering of images. The main result stated that, compared to mean filtering, median filtering and adapted-median filtering, the method we proposed was not only effective in denoising and keeping details of images, but also showing good performances in different SNR conditions.
机译:本文针对脉冲噪声污染的图像,提出了一种基于PCNN(脉冲耦合神经网络)的图像去噪滤波方法。该方法首先根据PCNN的火灾捕获特征确定被噪声污染的像素的位置。然后,在噪声像素的处理中应用了类似的中值滤波步骤。最后,根据噪声强度选择滤波器窗口的迭代次数和大小,实现了图像的自适应滤波。主要结果表明,与均值滤波,中值滤波和自适应中值滤波相比,我们提出的方法不仅在图像的去噪和保持细节方面有效,而且在不同的SNR条件下也表现出良好的性能。

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