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- Title
Primal-dual algorithm to solve the constrained second-order total generalized variational model for image denoising.
- Authors
Liu, Xinwei; Tang, Yuchao; Yang, Yixuan
- Abstract
The variational method, which is a popular approach for image denoising, aims to estimate the original image from a noisy or corrupted image. To consider the constraints of image pixel values fully, our study investigates a constrained second-order total generalized variational (TGV) model, which includes non-negative and bounded constraints as a special case. By adopting an equivalent definition of the second-order TGV, we transform the proposed constrained minimization problem into a minimization of the sum of two convex functions, where one is composed of a linear transformation. Subsequently, we employ the relaxed primal-dual proximity algorithm to solve it. The advantage of the obtained algorithm is that it is matrix-inversion free and does not involve any subproblem. Numerical results demonstrate that the performance of the constrained TGV model is slightly better than that of the unconstrained model.
- Subjects
IMAGE denoising; ALGORITHMS; PIXELS
- Publication
Journal of Electronic Imaging, 2019, Vol 28, Issue 4, p1
- ISSN
1017-9909
- Publication type
Article
- DOI
10.1117/1.JEI.28.4.043017