基于小波包变换与Wiener滤波的SMT焊点图像去噪技术

针对目前SMT(surface mount technology)焊点图像去噪效果不理想的问题,提出了一种基于小波包变换与wiener滤波的SMT焊点图像去噪新方法.利用小波包对图像进行分解,可以同时对SMT焊点图像的低频和高频部分进行多层分解,有利于保留图像信息,减少噪声对图像的影响.通过对图像的小波包系数的分析,对小波包树高频系数进行Wiener滤波,保留低频系数;然后进行小波包反变换,重构得到SMT焊点去噪后图像.实验表明,提出的方法不仅可以有效地去除SMT焊点图像的噪声,而且能很好地保留原图像的边缘信息,与传统方法相比,去噪性能和去噪声效果有一定的提高.

SMT Solder Joint Image Denoising Based on Wavelet Packet Transform and Wiener Filter

A novel image processing approach was proposed to reduce the noise in surface mount technology(SM7) solder joint image, based on wavelet packet transform and Wiener filter. At first, by the using wavelet packet transform, the approach not only decomposed the image into the low frequency part but also into the high frequency part of image in several scales so that the noise could be eliminated and the image useful information could be reserved. Then, after analyzing the wavclet packet tree coefficients, they were processed with the use of the Wiener filter, and leaved the wavelet packet tree low frequency coefficients without change. At last, the inverse wavelet packet transform was applied to reconstruct the SMT solder joint denoised image. The experimental results have illustrated that the proposed approach can accomplish a better result in image denoising compared with the conventional methods and can retain most image edges.