A first-order image denoising model for staircase reduction

In this paper, we consider a total variation-based image denoising model that is able to alleviate the well-known staircasing phenomenon possessed by the Rudin-Osher-Fatemi model (Rudin et al., Phys. D 60, 259-268, 30). To minimize this variational model, we employ augmented Lagrangian method (ALM). Convergence analysis is established for the proposed algorithm. Numerical experiments are presented to demonstrate the features of the proposed model and also show the efficiency of the proposed numerical method.