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- 저자 : CHEN Xiaxia (검색결과 2 건)
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An Improved Canny Edge Detection Algorithm
Yu Chen,Caixia Deng,Xiaxia Chen 보안공학연구지원센터 2015 International Journal of Hybrid Information Techno Vol.8 No.10
Aimed at the defects of the traditional Canny operator, this paper puts forward an improved algorithm in edge detection. First, this paper gives a new method on selfadaptive image block based on threshold value. Next, by proposing a new hybrid filterbank of self-adaptive median and morphology, we adopt this hybrid filter-bank to smooth the noise image. Then, we add the information of gradient in two bevel directions, so that the information of gradient is more complete. Last, by using the threshold value to process the image of gradient which is after non-maxima suppression, we obtain the image edge. For the noise image, this improved algorithm not only can filter out noise well, but also the image edge is continuous, smooth, clear. The experimental results show that the improved algorithm has a good effect in edge detection, strong capability of noise immunity. The objective evaluation and visual effect are good, too.
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The Improved Wavelet Threshold Function and Its Application
DENG Caixia,CHEN Xiaxia,LI Siqi,XU Yanxin 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.7
Images will produce noise in the process of storage and collection. Wavelet threshold de-noising is a simple and effective de-noising method, but the choice of threshold function is a key. The hard-threshold function is discontinuous and there is the deviation between the signal processed by the soft-threshold function and the real signal, so this paper constructs a new threshold function at the origin sufficiently smooth to deal with above problems. A parameter is added to the new threshold function, which is between the soft-threshold and hard-threshold function by adjusting the parameter. The new threshold function can remove the noise effectively, and the image information is well preserved. Hence it plays an important role in follow-up edge detection. The de-noising method with improved wavelet threshold is presented, and then uses morphological edge detection on the new image in this paper. The result shows that the method can detect the complete edge effectively, and the visual effect and objective evaluation are good.
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