Analysis of Denoising Method and Study of Denoising Fusion Optimization Algorithms for Industrial Gear Image

Abstract

: Aiming at the problem of noise filtering in the detection of industrial gear defects by machine vision technology, this paper makes some analysis and study for industrial gear image. For the analysis of denoising method, it uses the method of MATLAB numerical simulation to apply single noise (like Gauss noise, salt and pepper noise, multiplicative noise) to gear image, and uses median filter, mean filter, Gaussian smoothing filter and Wiener filter separately to filtering and compare the different filtering effects. For the study of denoising fusion optimization, a neighborhood mean method based on extremum median filter and a fusion filter method are proposed for the mixed noise. The simulation results show that the median filtering is the best for salt and pepper noise, the smooth filtering and Wiener filtering are better for Gauss noise and multiplicative noise, and the fusion filtering method with improved mean filtering is the best for gear images with mixed noise.