Underground image denoising method based on improved simplified pulse coupled neural network

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.

Keywords

• underground image
• video monitoring
• image processing
• image denoising
• salt and pepper noise
• pulse coupled neural network