结合空域噪声信息的小波脊提取算法

A Wavelet Ridge Extraction Algorithm Combined with Spatial Noise Information

小波变换轮廓术的实质是计算小波脊的位置,进而得到最佳伸缩尺度和相位信息。但小波脊的提取易受噪声的影响,从而造成小波脊提取不准确。针对此问题,提出了一种新的代价函数提取小波脊的算法。该算法利用小波变换系数幅值和尺度参数曲线梯度信息建立代价函数;使用Feature Scaling方法对小波变换系数幅值和尺度参数曲线梯度进行调整,平衡两项指标的权重;随着噪声增大,小波脊受噪声影响加大,结合空域噪声信息对调整后的尺度参数曲线梯度进行补偿,加大尺度参数曲线梯度权重,使脊线更加光滑。实验表明,该算法具有良好的抗噪能力和鲁棒性,能够较精确地提取小波脊。

The essence of wavelet transform profilometry is to calculate the position of the wavelet ridge, and then obtain the optimal scale and phase information. However, the extraction of wavelet ridge can be easily affected by noise, which can result in the inaccuracy of wavelet ridge extraction. Aiming at this problem, a new cost function is proposed to extract the wavelet ridge. The cost function is established by using wavelet transform coefficient amplitude and scale parameter curve gradient information. The feature scaling method is used to adjust wavelet transform coefficient amplitude and scale parameter curve gradient to balance the weight of the two indexes. The wavelet ridge will be affected more along with the noise increase. The spatial noise information is combined to compensate the adjusted scale parameter curve gradient, increase the weight of scale parameter curve gradient, and make the wavelet ridge smoother. The experiment shows that the algorithm has good anti-noise ability and robustness, and can extract the wavelet ridge accurately.