title: [DR image denoising based on Laplace-Impact mixture model].
abstract: A novel DR image denoising algorithm based on Laplace-Impact mixture model in dual-tree complex wavelet domain is proposed in this paper. It uses local variance to build probability density function of Laplace-Impact model fitted to the distribution of high-frequency subband coefficients well. Within Laplace-Impact framework, this paper describes a novel method for image denoising based on designing minimum mean squared error (MMSE) estimators, which relies on strong correlation between amplitudes of nearby coefficients. The experimental results show that the algorithm proposed in this paper outperforms several state-of-art denoising methods such as Bayes least squared Gaussian scale mixture and Laplace prior.
authors: Department of Automation, University of Science and Technology of China, Anhui, Hefei 230027.
authors: Guo-Dong
authors: GD
authors: Feng
authors: Xiang-Bin
authors: XB
authors: He
authors: He-Qin
authors: HQ
authors: Zhou
mesh_terms: D000465:Algorithms; D015233:Models, Statistical; D011856:Radiographic Image Enhancement / Q000379:methods*