A THz Image Edge Detection Method Based on Wavelet and Neural Network

Rong Wang, Lihua Li, Weijun Hong, N. Yang
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引用次数: 1

Abstract

A THz Image edge detection approach based on wavelet and Neural Network is proposed in this paper. First, the source image is decomposed by wavelet, the edges in the low-frequency sub-image are detected using Neural Network method and the edges in the high-frequency sub-images are detected using wavelet transform method on the coarsest level of the wavelet decomposition, the two edge images are fused according to some fusion rules to obtain the edge image of this level, it then is projected to the next level. Afterwards the final edge image of L-1 level is got according to some fusion rule. This process is repeated until reaching the 0 level thus to get the final integrated and clear edge image. The experimental results show that our approach based on fusion technique is superior to Canny operator method and wavelet transform method alone.
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基于小波和神经网络的太赫兹图像边缘检测方法
提出了一种基于小波和神经网络的太赫兹图像边缘检测方法。首先对源图像进行小波分解,在小波分解的最粗层次上利用神经网络方法检测低频子图像的边缘,利用小波变换方法检测高频子图像的边缘,根据一定的融合规则将两幅边缘图像融合得到该层次的边缘图像,然后将其投影到下一层次。然后根据一定的融合规则得到最终的L-1级边缘图像。重复这个过程,直到达到0级,从而得到最终的完整和清晰的边缘图像。实验结果表明,该方法优于单独的Canny算子方法和小波变换方法。
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