Multiplicative local binary patterns (MuLBP)

Speckle is a multiplicative noise that greatly deteriorates images. In this paper a model of Local Binary Patterns (LBP) adapted to images with speckle (MuLBP) is proposed. The multiplicative model is constructed by substituting the additive comparisons of the traditional LBP for multiplicative comparisons from the Bigeometric Calculus. The experiments were carried out considering the 10.824 images of the KTH-TIPS2, FMD, CASIA and UFI databases. To compare the additive and multiplicative models, the Euclidean distance between the LBP histograms of the image with noise and without noise is adopted. The results indicate that, the distance between the histograms, the image with noise with respect to the image without noise, is smaller for the multiplicative models than for the traditional additive models. The above means that, the multiplicative LBP represent in a better way the textures in images contaminated with speckle.