零件图像的Hilbert扫描-小波分解去噪处理

Analysis on The Depressing Noise Process of Hilbert Curve-Wavelet in Part Image

提出了利用图像的Hilbert扫描曲线和小波变换实现图像去噪的方法。将含噪声图像生成为Hilbert扫描矩阵,再将Hilbert扫描矩阵转换为一维向量,对一维向量进行小波分解,提取低频分量并转换为二维矩阵。然后,对二维矩阵进行Hilbert反扫描,完成图像去噪处理。仿真实验结果表明该方法是有效的。

It presents a image depressing noise method based on Hilbert curve in image scanning and wavelet analysis. It uses the noising image to generate Hilbert curve based on matrix, transforms the matrix into onedimensional vector, detects the wavelet low frequency components and transforms them into two- dimensional matrix. This matrix produces Hilbert inverse scanning and the noises of image are removed. Experiment results show that the method can efficiently remove the noises of image.