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We propose a set of iterative regularization algorithms for the TV-Stokesmodel to restore images from noisy images with Gaussian noise. These are someextensions of the iterative regularization algorithm proposed for the classicalRudin-Osher-Fatemi (ROF) model for image reconstruction, a single step modelinvolving a scalar field smoothing, to the TV-Stokes model for imagereconstruction, a two steps model involving a vector field smoothing in thefirst and a scalar field smoothing in the second. The iterative regularizationalgorithms proposed here are Richardson's iteration like. We have experimentalresults that show improvement over the original method in the quality of therestored image. Convergence analysis and numerical experiments are presented.

Iterative regularization algorithms for image denoising with the TV-Stokes model