# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 3 Proposed Methods
### Generative Network  
(1) Generator \(De\) is an integral component of the CDSR-CycleGAN model aimed at effectively removing the show-through effect from input images and restoring the original image. As illustrated in Figure 3, it consists of three sub-networks, including \(De_{\text{remove}}\), \(De_{\text{predict}}\), and \(De_{\text{refine}}\). The first stage includes \(De_{\text{remove}}\) and \(De_{\text{predict}}\). The primary role of the \(De_{\text{remove}}\) network is to initially eliminate the show-through effect of the image, making it easier for subsequent processes to restore the original image. The \(De_{\text{predict}}\) network is used to predict and locate foreground information, providing precise guidance for further processing. Lastly, in the second stage, the \(De_{\text{refine}}\) network adjusts and restores the image with removed show-through, further enhancing its visual appeal and realism. Specifically, the channel numbers of \(De_{\text{remove}}\) and \(De_{\text{predict}}\) are set to 64 for consistency, while the channel  
Figure 1: The framework for show-through removal. Figure 2: The framework for show-through generation. numbers of \(De_{\text{refine}}\) increase sequentially (64, 128, 256, 128, 64, and 3), capturing image features and details more effectively. Through the coordinated efforts of these three sub-networks, Generator \(De\) efficiently removes the show-through effect from input images and generates visually superior and detail-rich images. Both \(De_{\text{remove}}\) and \(De_{\text{predict}}\) take input images with show-through effects. Firstly, \(De_{\text{remove}}\) undergoes shallow feature extraction to obtain preliminary features. Next, a triple feature attention module (TFAM) with multiple skip connections is applied to achieve deeper feature attention. Subsequently, further processing is conducted using two convolutional networks and a long-skip global residual learning module. Ultimately, an image with the initial removal of the show-through effect is obtained. The TFAM network structure, illustrated in Figure 4, consists of three feature attention modules (FAMs) and a convolutional layer. The FAM network structure, shown in Figure 5, comprises local residual learning and channel-pixel attention (CPA) modules. Local residual learning allows less-important bleeding information to bypass through multiple local residual connections, while the main network focuses on effective foreground information. CPA retains shallow information and transfers it to deep layers, allowing \(De_{\text{remove}}\) to focus more on recovering effective information such as show-through overlapping regions, detail textures, and color accuracy. This not only enhances the restoration accuracy of \(De_{\text{remove}}\) but also reduces redundancy and improves efficiency. Figure 4: The network architecture of TFAM. Figure 5: The network architecture of FAM. Figure 3: The network architecture of generator \(De\). The \(De_{\text{predict}}\) network is used to predict and locate foreground information. Firstly, the receptive field is expanded through four sets of dilated convolutions and standard convolution operations to enable the network to capture more contextual information. This enhances the accuracy and precision of foreground prediction. Next, the global and local features of the \(De_{\text{predict}}\) network are integrated through long paths to extract more robust features. Attention mechanisms are then employed to quickly capture foreground information hidden in complex backgrounds. First, a \(1\times 1\) convolutional layer compresses the features of layer 13 into a vector, which is used to adjust the feature representation of the previous step as weights. These adjusted weights are element-wise multiplied with the output of layer 12 to extract more significant foreground features. Finally, the extracted foreground features are reconstructed together with the original input image to obtain a clear image.