An Efficient Primal-Dual Method for (LTV)-T-1 Image Restoration
Image restoration based on an l(1)-data-fitting term and edge preserving total variation regularization is considered. The associated nonsmooth energy minimization problem is handled by utilizing Fenchel duality and dual regularization techniques. The latter guarantee uniqueness of the dual solution and an efficient way for reconstructing a primal solution, i.e., the restored image, from a dual solution. For solving the resulting primal-dual system, a semismooth Newton solver is proposed and its convergence is studied. The paper ends with a report on restoration results obtained by the new algorithm for salt-and-pepper or random-valued impulse noise including blurring. A comparison with other methods is provided as well.
total variation regularization duality semismooth newton random-valued impulse noise deblurring l(1)-data fitting salt-and-pepper noise