Inverse halftoning with nonlocal regularization

Conventional wisdom in inverse halftoning heavily relies on the assumption about the local smoothness of image signals. Motivated by the effectiveness of nonlocal denoising, we propose a new class of inverse halftoning techniques using nonlocal regularization in this paper. The continuous-tone image is characterized by the intersection of two constraint sets - one related to the quantization process of halftoning and the other specified by nonlocal similarity-based regularization. Our nonlocal inverse halftoning algorithms alternatively project onto these two constraint sets; since the nonlocal regularization constraint set is nonconvex, we have borrowed the idea of deterministic annealing to optimize the performance of the proposed technique. Our experimental results have shown that our nonlocal approach can significantly outperform several existing state-of-the-art techniques in terms of both subjective and objective qualities.