Image edge detection based on pulse coupled neural network and modulus maxima in non-subsampled contourlet domain

Edge is the intrinsic geometric structure of an image. Edge detection methods are the key technologies in the lleld of image processing. In this paper, a multi-scale image edge detection method is proposed to effectively extract image geometric features. A source image is decomposed into the high frequency directional sub-bands coefficients and the low frequency sub-bands coefficients by non-subampled contourlet transform （NSCT）. The high frequency sub-bands coefficients are used to detect the abundant details of the image edges by the modulus maxima （MM） algorithm. The low frequency sub-band coefficients are used to detect the basic contour line of the image edges by the pulse coupled neural network （PCNN）. The final edge detection image is reconstructed with detected edge information at different scales and different directional sub-bands in the NSCT domain. Experimental results demonstrate that the proposed method outperforms several state-of-art image edge detection methods in both visual effects and objective evaluation.