Underground image denoising method based on improved simplified pulse coupled neural network
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摘要: 针对传统图像去噪方法易使图像模糊和丢失边缘信息等问题,根据煤矿井下视频图像光度不均、噪声较大的特点,提出采用基于改进的简化脉冲耦合神经网络对煤矿井下图像进行去噪处理。对简化的脉冲耦合神经网络模型中神经元连接强度β的选取方法进行改进,使β依赖于图像像素灰度值,从而更加有效地去除椒盐噪声;对动态门限的衰减时间常数αE的选取方法进行改进,使αE依赖阈值输出的放大系数vE,减少整个模型的参数,并通过实验选取vE值。实验结果表明,与传统的中值滤波、均值滤波方法相比,基于改进的简化脉冲耦合神经网络的去噪方法不仅有效去除了矿井图像的椒盐噪声,而且很好地保持了图像的边缘等细节特征。Abstract: In order to solve problems of traditional image denoising methods such as image blur, edge information loss and so on, an image denoising method based on improved simplified pulse coupled neural network was proposed according to characteristics of underground images including uneven luminosity and large noise. Selection of neurons joining strength β was improved, which made β depend on pixel gray value of image, so as to get better denoising effect. At the same time, selection of decay time constant αE of dynamic threshold was improved, which made αE depend on amplification coefficient vE of threshold output, so as to reduce number of parameters of simplified pulse coupled neural network model. The value of vE was selected through experiment. The experiment results show that the method removes salt and pepper noise of underground images more effectively and preserves details of image edge more completely than traditional median filtering and mean filtering.
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