Improving Wavelet Threshold De-noising Applied on Parts Detection

School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai, 201600, China

Abstract

The traditional filtering methods such as median filter and mean filter always blurrs image features, resulting in poor noise reduction effect. Wavelet transform has unique adaptability due to its variable resolution, which can better implement wavelet denoising on the basis of image feature. Aiming at the shortcoming of traditional wavelet transform threshold denoising, based on the hard threshold and soft threshold function, this paper proposes improved adaptive thresholding function. By comparing and validating, this method obtains the smaller mean square error (MSE) and higher peak signal to noise ratio. Meanwhile, this method improves the quality of detection images, and reduces the impact on images brought by noise from external enviroment and internal system. So, this can be applied to image noise reduction of the detection system.