Abdominal Ct Image Denoising Based on a Laplace Distribution With Local Variance in Steerable Pyramid Domain Publisher



Rabbani H¹
Source: 5th Int. Conference on Information Technology and Applications in Biomedicine, ITAB 2008 in conjunction with 2nd Int. Symposium and Summer School on Biomedical and Health Engineering, IS3BHE 2008 Published:2008

Abstract

This paper presents a new image denoising algorithm based on the modeling of coefficients in each subband of steerable pyramid with a Laplacian probability density function (pdf) with local variance. This pdf is able to model the heavy-tailed nature of steerable pyramid coefficients and the empirically observed correlation between the coefficient amplitudes. Within this framework, we describe a novel method for image denoising based on designing a maximum a posteriori (MAP) estimator, which relies on the zero-mean Laplaclan random variables with high local correlation. Despite the simplicity of our spatially adaptive denoising method, both in its concern and implementation, our denoising results achieves better performance than several published methods such as Bayes least squared Gaussian scale mixture (BLS-GSM) technique that is a state-of-the-art denoising technique ©2008 IEEE.