Deep Learning based Inverse Halftoning via Stationary Wavelet Domain

Inverse halftoning aims to reconstructs the continuous-tone image, i.e. 255 discrete color levels from its halftone image. The main challenge of inverse halftoning task is on removing the noisy dots over flat areas and, at the same time, restoring the image structures and texture details. To tackle this problem, we propose a novel approach to reconstruct the halftone image in wavelet domain, namely Subbands Aware Inverse Halftoning Network (SAIHN). The proposed framework consists of two sub networks, i.e. the first networks adjust the wavelet sub-bands for image reconstruction, whereas, the second networks refines the quality of reconstructed image by correcting the distortion in pixel domain. The experimental results confirm that the proposed method can reconstruct the halftone image with high quality measured in term of objectively and subjectively assessments. Furthermore, the proposed method demonstrates its superiority on less learnable parameters and more efficient computational complexity in comparison to that of the former existing deep learning-based reconstruction method.