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Owing to image noise and differ in thousands ways of edge shape, traditional morphological edge detection operators can't detect the edge well and the edge detected by it is very blur. In order to gain the good edge, characteristic of morphological operators are used. Generalized morphological detection operator is used to conquer noise influence, Omnidirectional edge operator is used to detect different direction edge information and multi-scale edge operator is used to gain different scale and detail edge information and wipe off noise more. After that, according to suitable weights combining edge result to make up another edge image which can gain final edge through blur-minimization edge detection. Experiment proved that the proposed edge detection algorithm can detect edge better than traditional detection algorithm and can not only detect image edge effectively with upper detection precision but also restrain noise efficaciously.

Omnidirectional Multi-scale Generalized Blur Minimization Mathematical Morphology Edge Detection Algorithm