Image Edge Detection Based on Lifting Wavelet

The current widely used edge detection algorithm have Sobel, Roberts, Laplacian etc. The core idea of these algorithms is that the edge points correspond to the local maximal points of original image’s gray-level gradient. However, when there are noises in images, these algorithms are very sensitive to noises, and may detect noise points as marginal points, and the real edge may not be detected because of the noises’ interference. Designing image edge detection systems with good nature is a goal which all researchers pursue. Method of lifting can best deal with based on MRA Multi-resolution Analysis method which is time and memory consuming which impedes its real-time application. The process of lifting method was introduced. Method of image edge detection based on lifting wavelet was introduced. The algorithm, traditional wavelet image edge detection algorithms and canny operator are compared. Result of the experiment indicate, the quality of image have been improved. It is better than traditional wavelet and canny operator image edge detection algorithms.

[1]  Shan Jie.  Image Compression Based on Fast Lifting Wavelet Transform , 2005 .

[2]  Mohamed Ali,et al.  Using the Canny edge detector for feature extraction and enhancement of remote sensing images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[3]  Paul Abbott,et al.  Automation of the lifting factorisation of wavelet transforms , 2000 .

[4]  Michael Smith,et al.  Combining spatial and scale-space techniques for edge detection to provide a spatially adaptive wavelet-based noise filtering algorithm , 2002, IEEE Trans. Image Process..

[5]  Kar-Kin Lee,et al.  Chin contour estimation using modified Canny edge detector , 2002, 7th International Conference on Control, Automation, Robotics and Vision, 2002. ICARCV 2002..

[6]  Wim Sweldens,et al.  The lifting scheme: a construction of second generation wavelets , 1998 .

[7]  Hongjian Shi,et al.  Canny edge based image expansion , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).

[8]  Azriel Rosenfeld,et al.  Optimal edge-based shape detection , 2002, IEEE Trans. Image Process..

[9]  C.J. Vianna,et al.  Combining Marr and Canny operators to detect edges , 2003, Proceedings of the 2003 SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference - IMOC 2003. (Cat. No.03TH8678).