Dual-tree Complex Wavelet Transform for Chip Images Threshold Denoising
基于双树复数小波变换的切屑图像阈值去噪

Chip analysis is very important for chip control. In order to get chip parameters such as chip flowing direction and chip sideward-curl radius as well, charge coupled device (CCD) is often used to detect chip images. During machine operation, there are a lot of additive noises in the chip images which are detected by CCD. In order to de-noise and get edges of chip, a method based on dual-tree complex wavelet transform threshold is described in this paper. Because 2D dual-tree complex wavelet transform produces six sub bands at each scale, each of which are strongly oriented at distinct angles, it has significant advantages over real wavelet transforms for certain image processing problems. It has improved directionality and reduced shift sensitivity, which is important in chip image processing. In addition, the original chip image isn't known generally. Wavelet threshold estimation by generalized cross validation(GCV) for chip images denoising is described in the paper. Some examples are given at end of this paper, proving that the methods can improve the ability of denoising and get better edge of chip.