Augmented Lagrangian method for generalized TV-stokes model
Tai, Xue Cheng
Date of Issue2011
School of Physical and Mathematical Sciences
In this paper, we propose a general form of TV-Stokes models and provide an efficient and fast numerical algorithm based on the augmented Lagrangian method. The proposed model and numerical algorithm can be used for a number of applications such as image inpainting, image decomposition, surface reconstruction from sparse gradient, direction denoising, and image denoising. Comparing with properties of different norms in regularity term and fidelity term, various results are investigated in applications. We numerically show that the proposed model recovers jump discontinuities of a data and discontinuities of the data gradient while reducing stair-case effect.
Journal of scientific computing
© 2011 Springer Science+Business Media, LLC.