[1]YAN Haipeng,WU Yuhou.Filtering image impulse noise by using a PCNN image noise reduction technique[J].CAAI Transactions on Intelligent Systems,2017,12(2):272-278.[doi:10.11992/tis.201605027]
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Filtering image impulse noise by using a PCNN image noise reduction technique

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 2 Page number: 272-278 Column: 学术论文—机器感知与模式识别 Public date: 2017-05-05
Title:
Filtering image impulse noise by using a PCNN image noise reduction technique
Author(s):
YAN Haipeng1;  WU Yuhou2
1. School of Mechanical Engineering, Shenyang Jianzhu University, Shenyang 110168, China;
2. National-Local Joint Engineering Laboratory of High-Grade Stone Numerical Control Machining Equipments and Technology, Shenyang Jianzhu University, Shenyang 110168, China
Keywords:
image noise reduction; pulse coupling neural network; synaptic link strength; threshold function; resolving power
CLC:
TP391
DOI:
10.11992/tis.201605027
Abstract:
Traditional methods for image noise reduction typically damage the edges and details of an image, blur image contours, and thereby make them indistinct after image noise reduction is complete. To achieve better results in image noise reduction, we propose a pulse coupling neural network (PCNN) image noise reduction method based on a modified synaptic link strength and a modified threshold function. We simplified the basic PCNN model and adaptively changed the synaptic link strength value; further, we improved the threshold function by using a segmented attenuation function so as to improve the resolving power for different gray values of the given images. We improved the accuracy of our algorithm for identifying noise by positioning noise points according to the difference of firing times between the neuron and its surrounding neurons. Using this approach, we achieved better noise reduction results; our experimental results showed that our proposed method was able to accurately identify image impulse noise points and effectively remove these noise points. Further, through subjective evaluation, we observed that image edge details were also protected.
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