Multi-scale decomposition enhancement algorithm for surface defect images of Si3N4 ceramic bearing balls based on stationary wavelet transform

Abstract

ABSTRACT In order to improve the detection efficiency and image quality of Si3N4 ceramic bearing balls surface defects, digital image processing technology is used to analyse the information characteristics of Si3N4 ceramic bearing balls surface. A multi-scale decomposition enhancement algorithm for surface defect images of Si3N4 ceramic bearing balls based on the stationary wavelet transform is proposed. By building the surface defects detection system of Si3N4 ceramic bearing balls, the image enhancement program based on stationary wavelet transform with index low-pass filtering and nonlinear transform enhancement is designed. Finally, the effectiveness of the algorithm is verified by experiments. The experimental results show that the algorithm is applied to the surface defects image of Si3N4 ceramic bearing balls can effectively weaken the background noise and surface grinding texture, and enhance the contrast between defects and background clearly. In addition, the binary image is obtained by an adaptive threshold binary algorithm. After removing the tiny points by morphological opening operation, the defects are accurately and completely segmented, and then the Canny operator is used for edge detection to extract the edge contour of defects. When the decomposition level is set to 3, the average calculation time is 0.88 s, which are relatively short and have sufficient precision, and the algorithm can be extended to other kinds of ceramic ball surface damage detection.