Bayesian denoising of white light interference signal in rough surface measurement

Abstract

The measurement and quantitative analysis of three-dimensional microtopography of failure surface has important values for understanding the initiation and development of failure process. In this paper, we used scanning white-light interferometry (SWLI) for the characterization of metallic failure surface, but for it’s large height variation, non-homogeneous and weak spectral reflectivity, the detected interferometric signals would be severely disturbed by the noises, consequently causing large recontruction error. For this reason, we used a novel denoising method based on Bayesian estimation to process the extremely weak white light interference signal, theoretical and algorithmic derivations were given out as well. Finally, the effect of signals denoising was evaluated and a quantitative comparison with other typical approaches showed that the Bayesian estimation method had obvious advantage in the signal-to-noise ratio improvement and made much more reduction in the mean squared error, along with a better smoothness and stability.