Proceedings Article10.1109/CISP.2015.7407900
An improved edge detection algorithm based on mathematical morphology and directional wavelet transform
Weiguo Zhang,Dan Shi,Xiaoqiang Yang +2 more
- 01 Oct 2015
- pp 335-339
11
TL;DR: An improved edge detection algorithm based on the mathematical morphology algorithm and the directional wavelet transform is proposed that can well suppress the noise interference and get a more continuous and complete edge image comparing to other methods.
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Abstract: Traditional methods of the edge detection can't completely extract the low frequency image edge. It is easy for them to discard some important information in the high frequency sub-images. If there is much noise in the image, these methods can not entirely eliminate the noise. We will get the poor image edge because of the noise. In order to solve these problems, we respectively analyze the theories of the mathematical morphology algorithm and the directional wavelet transform. We propose an improved edge detection algorithm based on the mathematical morphology algorithm and the directional wavelet transform. We firstly use the binary mathematical morphology to de-noise and detect the edge of the low frequency sub-image in the wavelet domain. Then we de-noise the high frequency sub-image in horizontal, vertical and diagonal directions. We adopt the Canny operator to detect the edge of the high frequency image. The low frequency edge image and the high frequency edge image can be respectively got by the above algorithms. Finally we can get a complete and continuous edge by using some fusion rules. The results show that the improved edge detection algorithm can well suppress the noise interference. It also can get a more continuous and complete edge image comparing to other methods.
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Gauss Noise Image Recovering Method Based on Directional Wavelet Transform
ZHANG Zhen,MA Si-liang,TAN Kun +2 more
TL;DR: A directional wavelet transform-based method is proposed for Gaussian noise removal from images, outperforming standard wavelet-based methods in PSNR and visual quality, while reducing Gibbs effect and preserving image edges.