# Removal of Color-Document Image Show-Through Based on Self-Supervised Learning
## 3 Proposed Methods
### Generative Network
\(De_{\text{refine}}\) consists of an encoder and decoder, using a encoder-decoder structure similar to that of CycleGAN. Nine context-aware blocks (CAB) are stacked between the encoder and decoder to increase the receptive field and contextual dependency, as shown in Figure 6. CAB achieves this end through feature transformation, a context-attention mechanism, and residual connections. The encoder and decoder are connected through residual connections, allowing the \(De_{\text{refine}}\) network to be end-to-end mapped and speeding up convergence while improving the quality of generated images. In the \(De_{\text{refine}}\) network, the encoder extracts features and downsamples the input image through components such as convolution, batch normalization (IN), a ReLU activation function, dropout, and max pooling. The decoder then upsamples and reconstructs the features extracted by the encoder through components such as transposed convolution, IN, the ReLU activation function, and dropout. (2) Generator \(Re\)  
The generator \(Re\) (show-through-generation network) is another component of the CDSR-CycleGAN model, and its network structure is shown in Figure 7. Its role is to take in input images without the show-through effect and generate the corresponding show-through images. Unlike the other generator \(De\), which focuses on generating cycle mappings, the generator \(Re\) specifically focuses on generating show-through mappings. \(Re\) is the network used for show-through generation and consists of two sub-networks: the foreground prediction network \(Re_{\text{predict}}\), which predicts and locates the foreground information, and the background blur network \(Re_{\text{blur}}\), which performs blurring on the background. Similar to \(De_{\text{predict}}\), \(Re_{\text{predict}}\) has a similar network structure but differs in the way it reconstructs the image. \(Re_{\text{blur}}\) has a network structure similar to \(De_{\text{refine}}\) but serves a different purpose. \(Re_{\text{predict}}\) combines the extracted foreground features with the original input image to obtain a clear foreground image with known foreground information positions. \(Re_{\text{blur}}\) is used to blur the input image and provides a foundation for adding the back-to-front interference effect. Subsequently, the show-through effect is simulated around the predicted foreground information by further enhancing the realism of the image. Figure 6: The network architecture of CAB. (3) Similarity Network \(S\)  
The similarity network \(S\) is a crucial component of the CDSR-CycleGAN model. In the absence of labeled values, this paper utilizes \(S\) to provide unsupervised similarity labels and compute pseudo-similarity loss. In practical applications, \(S\) is employed to generate similarity images for both the \(De\) generator and the \(Re\) generator. Therefore, it is referred to as the similarity-\(De\) network \(S\_De\) in the \(De\) architecture and the similarity-\(Re\) network \(S\_Re\) in the \(Re\) architecture. The architecture of \(S\) is illustrated in Figure 8, with a similar application for \(Re\) as for \(De\). Firstly, a \(7\times 7\) initial convolution operation is applied, followed by instance normalization and ReLU activation. Subsequently, downsampling is performed twice using \(3\times 3\) convolution kernels, IN, and ReLU activation, gradually increasing the number of channels. Then, feature extraction is conducted through nine CABs, as shown in Figure 6. Finally, two upsampling operations are performed using deconvolution blocks, each consisting of a \(3\times 3\) deconvolution operation, IN, ReLU activation, and a gradual reduction in channel numbers. Ultimately, the output of network \(S\) is generated using a \(7\times 7\) convolution kernel and mapped to the range \((-1,1)\) through the tanh activation function. Through the combined action of the \(De\) generator, the \(Re\) generator, and the similarity network \(S\), the CDSR-CycleGAN model achieves high-quality show-through removal and show-through generation.