title: Hybrid regularizers-based adaptive anisotropic diffusion for image denoising.
abstract: To eliminate the staircasing effect for total variation filter and synchronously avoid the edges blurring for fourth-order PDE filter, a hybrid regularizers-based adaptive anisotropic diffusion is proposed for image denoising. In the proposed model, the [Formula: see text]-norm is considered as the fidelity term and the regularization term is composed of a total variation regularization and a fourth-order filter. The two filters can be adaptively selected according to the diffusion function. When the pixels locate at the edges, the total variation filter is selected to filter the image, which can preserve the edges. When the pixels belong to the flat regions, the fourth-order filter is adopted to smooth the image, which can eliminate the staircase artifacts. In addition, the split Bregman and relaxation approach are employed in our numerical algorithm to speed up the computation. Experimental results demonstrate that our proposed model outperforms the state-of-the-art models cited in the paper in both the qualitative and quantitative evaluations.
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, 23009 Hefei, China ; The Key Lab of Intelligent Perception and Computing of Anhui Province, Jixian Road, 246011 Anqing, China.
authors.forename: Kui
authors.initials: K
authors.lastname: Liu
authors.affiliation: School of Computer and Information, Hefei University of Technology, Tunxi Road, 23009 Hefei, China.
authors.forename: Jieqing
authors.initials: J
authors.lastname: Tan
authors.affiliation: The Key Lab of Intelligent Perception and Computing of Anhui Province, Jixian Road, 246011 Anqing, China.
authors.forename: Liefu
authors.initials: L
authors.lastname: Ai
keywords: Fourth-order filter; Image denoising; Relaxation method; Split Bregman method; Total variation