Inverse Halftoning Based on Weighted Nuclear Norm Minimization

Abstract

The inverse halftoning technology refers to restoring a continuous-toned image from a halftoned image with only bi-level pixels. However, recovering the continuous images from their halftoned ones is normally ill-posed, which makes the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are twofold. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-toned image without destroying the image details and structures. The experimental results showed that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance. The codes and constructed models are available at: https://github.com/juneryoung2022/IH-WNNM.