Spatial‐temporal fusion for flotation froth image denoising based on BLS‐GSM method in curvelet domain
Jinping Liu, Weihua Gui, Zhaohui Tang, Qing Chen · IEEJ Transactions on Electrical and Electronic Engineering · 2013
Abstract The visual appearance of flotation froth surface involves a major cue about the flotation performance, which is significant for machine‐vision‐based flotation process monitoring and control. However, the froth image suffers from noise contamination inevitably, which incurs serious negative effects on the visual feature extraction of froth images. This paper presents a spatial‐temporal image denoising scheme based on statistical modeling of the froth image in the Curvelet domain and weighted processing of the relative patches of adjacent image sequences. First, the Gaussian scale‐mixture model of the image coefficients in the local spatial neighborhood is investigated according to their statistical distribution property to get the clean coefficients based on inner‐frame content by using Bayesian least‐squares estimation. Then, the temporal patches from adjacent image sequences are performed with weighted impact factors according to the similarities of the relative patches after motion compensation. Thus, clean image coefficients based on spatial‐temporal content are achieved. This method is validated by the simulated additive noise removal and the real industrial image processing. The results of simulation and real application of image noise elimination reveal the excellent performance of this method, which can effectively reconstruct the froth image while protecting more bubble details for the following froth description. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.