An Edge Detection Method for Strong Noisy Image Using Shearlets
Numerous edge detection methods have been proposed to detect image edges. However, these methods are not very effective in detecting edges in strong noisy images. Recent years, multiscale analysis has been introduced to the realm of image processing. As the third generation wavelet, shearlets have their own superiority. Anisotropic dilation operator and shear operator are introduced to overcome the shortcomings of traditional wavelets. Because of their sensitivity to directions, shearlets are apt to do the job of edge detection. Based on shearlets, in this paper, a new edge detection method is proposed. The main idea about this new method is combining the shearlet denoising method with the edge detecting method based on shearlets. Analyzing results show that edges are characterized as zerocrossing points in shearlet domain and can be extracted from shearlet transform coefficients by detecting zero crossing points and using boundary tracking method. Many experiments are conducted to test this novel approach and we also compare Sobel, Log and Canny operators with this new method. Experiments demonstrate that when an image existing high deviation Gaussian noise, this method are much better than ordinary edge detection operators in time domain.
Edge detection shearlet transform image denoising zerocrossing points boundary tracking
Yuming Li Hanqiang Cao Zijian Xu
Dept. of Elec. & Info. Eng., HUST, Wuhan, 430074 Dept. of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands, NL - 56
国际会议
桂林
英文
1-8
2011-11-01（万方平台首次上网日期，不代表论文的发表时间）