基于双树复数小波变换的切屑图像阈值去噪

由于在金属切削加工时通过CCD相机采集的切屑图像含有大量噪声，因此如何有效地去除噪声，获取切屑边缘信息是分析切屑形态参数，实现切屑控制的关键。目前小波变换图像去噪效果较好，而复数离散小波变换比实数小波变换具有更多优势，如：平移不变性、方向性等。并可提高图像的去噪能力。为了提高切屑图像的去噪能力，提出了一种采用双树复数小波变换进行切屑图像去噪的方法，即在原信号(噪声标准方差)未知情况下，采用GCV准则选取去噪阈值，双树复数小波变换进行去噪。典型图像与切屑图像去噪结果显示，该方法能有效地提高金属切削加工过程中切屑图像的噪声去除及屑形边缘检测的能力。

Dual-tree Complex Wavelet Transform for Chip Images Threshold Denoising

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.