  We propose a set of iterative regularization algorithms for the TV-Stokes
model to restore images from noisy images with Gaussian noise. These are some
extensions of the iterative regularization algorithm proposed for the classical
Rudin-Osher-Fatemi (ROF) model for image reconstruction, a single step model
involving a scalar field smoothing, to the TV-Stokes model for image
reconstruction, a two steps model involving a vector field smoothing in the
first and a scalar field smoothing in the second. The iterative regularization
algorithms proposed here are Richardson's iteration like. We have experimental
results that show improvement over the original method in the quality of the
restored image. Convergence analysis and numerical experiments are presented.
