An approximate sparsity model for inpainting

Existing sparse inpainting models often suffer from their over-constraints on the sparsity of the transformed recovered images. Due to the fact that a transformed image of a wavelet or framelet transform is not truly sparse, but approximately sparse, we introduce an approximate sparsity model for inpainting. We formulate the model as minimizing the number of nonzero components of the soft-thresholding operator applied to the transformed image. The key difference of the proposed model from the existing ones is the use of a soft-thresholding operator which shrinkages the components of the transformed image. To efficiently solve the resulting nonconvex optimization problem, we rewrite the l(o) norm, which counts the number of nonzero components, as a weighted l(1) norm with a nonlinear discontinuous weight function, which is then approximated by a continuous weight function. We overcome the nonlinearity in the weight function by an iteration which leads to a numerical scheme for solving the nonconvex optimization problem: In each iteration, we solve a weighted l(1) convex optimization problem. We then focus on understanding the existence of solutions of the weighted l(1) convex optimization problem and characterizing them as fixed-points of a nonlinear mapping. The fixed-point formulation allows us to employ efficient iterative algorithms to find the fixed-points. Numerical experiments are shown to demonstrate improvement in performance of the proposed model over the existing models for image inpainting. (C) 2013 Elsevier Inc. All rights reserved.