# Inverse Halftoning Methods Based on Deep Learning and Their Evaluation Metrics: A Review
## 3 Inverse Halftoning Technology for Scanned Halftone Images
### Inverse Halftoning Based on Special Designed DCNNs for Scanned Halftone Images  
During the process of printing and scanning, the scanned halftone image quality is seriously degraded, and there is no paired real data for machining learning. Thus, the inverse halftoning for scanned halftone images is more difficult than the method for the digital halftone image. None of the existing DCNN methods can satisfy for scanned halftone images. Recently, only a few studies have addressed this problem, such as in references [24; 29; 30], where the DCNN is specially designed for inverting scanned halftone images. In terms of scanned halftone images, they are often contaminated with moire patterns. Considering the moire patterns span over a wide range of frequencies, [PERSON] et al. [29] proposed a multi-resolution DCNN for moire pattern removal. The proposed architecture is shown in Figure 14, in which the input image is firstly converted into multiple feature maps at different resolution levels by a nonlinear multi-resolution analysis, and then each feature map is fed into a sequence of cascaded convolutional layers for removing the moire pattern associated with the specific frequency band of this branch, and finally the outputs at different resolutions are up-sampled as the input resolution and fused together as the final output image. The method of the reference [29] is a groundbreaking work for moire pattern removal, which achieved the state-of-the-art performance in this field at that time. However, there is still room for improving image details. Another famous work is the research proposed in reference [30], which can not only remove halftone artifacts but also synthesize the fine details lost during halftone. The network is depicted as shown in Figure 15, which combines the GAN network with other networks, such as semantic segmentation network [37] and U-Net [38]. The method consists of two stages. In the first stage, the initial inverse halftone image is generated by applying CoarseNet, semantic segmentation network [37], and EdgeNet rationally. CoarseNet, which consists of U-Net and skip connection, is used to reconstruct colors, tones, and shapes roughly. The semantic segmentation network [37] is adopted to generate object-level context and extract edge information by the EdgeNet. In the second stage, the DetailsNet, which also consists of common convolutional layer, is used to enhance details and further remove artifacts in  
Figure 14: Multi-resolution pyramid networks for moiré pattern removal [29]. Figure 13: The network architecture in reference [28]. TI, GI, TIF, and GIF refer to target image, guidance image, target image feature, and guidance image feature, respectively. images. To improve the quality of the inverse halftone image, two discriminators [39] were adopted, where one is used to detect the whole image while the other is to examine local regions. Compared to previous methods, the proposed method can achieve more convincing results, but this is dependent on the training examples. Aiming at the restoration of invaluable historical photographs in the form of halftone prints in old publications, [PERSON] et al. [24] proposed a pioneering method to address this issue. The challenge is that the quality of the scanned halftone image is poor and no paired real data are available for training. To solve these problems, the authors [24] developed a novel learning strategy by dividing the task into two stages as shown in Figure 16. The first stage is to remove printing artifacts, where the scanned halftone image is transformed to a digital halftone image. Due to lack of paired data, the CycleGAN technique is employed in the sub-network used in the first stage, which is an unsupervised learning network including U-Net structure and skip connection. Through the first stage, the scanned halftone image is converted into a synthetic halftone image. In the second stage, the synthetic halftone image can be converted into a continuous-tone image by inverse halftoning method for digital halftone images.