Surface Defect Edge Detection Based on Contourlet Transformation
In order to preferably capture the surface defect edge information of strip steel, and provide more accurate data for subsequent defect analysis, this paper researches on the principle and implementation method of Contourlet transformation, analyzes the multi-directional and anisotropic characteristics, and proposes the image edge detection algorithm based on Contourlet transformation. This paper uses Laplacian Pyramid filter and directional filter for combination to realize Contourlet filter bank, and extracts the image edge information by the way of detecting the method of modulus maximum of Contourlet sub-band coefficient. The comparative experiments of the edge detection algorithm based on wavelet transformation and Sobel algorithm prove that the defect edge extracted by this algorithm is closer to the true edge of surface defect.