Inverse halftoning through structure-aware deep convolutional neural networks

• Published in: Signal processing Vol. 173; p. 107591
• Main Author: Son, Chang-Hwan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.08.2020
• Subjects: Convolutional neural networks; Dictionary learning; Image filtering; Inverse halftoning; U-net; Inverse halftoning; Image filtering; Convolutional neural networks; U-net; Dictionary learning
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2020.107591

•A structure-aware convolutional neural network is proposed for inverse halftoning.•Proposed network can be trained adaptively to local image structures.•Proposed network can increase detail representation and dot removal.•Proposed method surpasses the conventional state-of-the-art methods. The primary issue in inverse halftoning is removing noisy dots on flat areas and restoring image structures (e.g., lines, patterns) on textured areas. Hence, a new structure-aware deep convolutional neural network that incorporates two subnetworks is proposed in this paper. One subnetwork is for image structure prediction while the other is for continuous-tone image reconstruction. First, to predict image structures, patch pairs comprising continuous-tone patches and the corresponding halftoned patches generated through digital halftoning are trained. Subsequently, gradient patches are generated by convolving gradient filters with the continuous-tone patches. The subnetwork for the image structure prediction is trained using the mini-batch gradient descent algorithm given the halftoned patches and gradient patches, which are fed into the input and loss layers of the subnetwork, respectively. Next, the predicted map including the image structures is stacked on the top of the input halftoned image through a fusion layer and fed into the image reconstruction subnetwork such that the entire network is trained adaptively to the image structures. The experimental results confirm that the proposed structure-aware network can remove noisy dot-patterns well on flat areas and restore details clearly on textured areas. Furthermore, it is demonstrated that the proposed method surpasses the conventional state-of-the-art methods based on the deep convolutional neural network, U-Net, and locally learned dictionaries.