Circular Symmetric Laplacian Mixture Model in Wavelet Diffusion for Dental Image Denoising

In this paper, we try to find a particular combination of wavelet shrinkage and nonlinear diffusion for noise removal in dental images. We selected the wavelet diffusion and modified its automatic threshold selection by proposing new models for speckle-related modulus. The Laplacian mixture model, Rayleigh mixture model, and circular symmetric Laplacian mixture models were evaluated and, as it could be expected, the latter provided a better model because of its compatibility with heavy tailed structure of the wavelet coefficients besides their interscale dependence. The numerical evaluation of contrast-to-noise ratio (CNR) along with simple observation of the results showed reasonably acceptable improvement of CNR from 2.9149 to 38.8813 in anterior--posterior images, from 41.6131 to 86.3141 in cephal–lateral images, from 13.6414 to 43.4711 in intraoral pictures, and from 6.0102 to 31.8771 in panoramic datasets. Furthermore, technical ability of the proposed filtering method in retaining the possible cavities on dental images was evaluated in two datasets with natural and artificially applied cavities.