# Suppression of Multiple Reflection Interference Signals in GPR Images Caused by Rebar Using VAE-GAN
## 2 Materials and Methods
### 2.2. CSA Module  
Due to its metallic properties, rebar generates strong, high-amplitude reflective signals in GPR images, typically appearing as bright, high-contrast lines or points. In contrast, voids produce weaker reflective signals with lower amplitude, appearing as darker areas or lighter lines. To better suppress rebar clutter and achieve more effective reconstruction of void signals, the channel and spatial attention module (CSA) was designed in the encoder-generator pairs \(\{E_{A},G_{A}\}\), \(\{E_{A},G_{B}\}\), \(\{E_{B},G_{A}\}\), and \(\{E_{B},G_{B}\}\), as shown in Figure 2a. The CSA module is inspired by the Convolutional Block Attention Module (CBAM), which integrates both channel and spatial attention mechanisms. Similarly, CSA adopts this dual attention strategy to optimize feature extraction and processing from both the channel and spatial dimensions [27]. The module inputs features from two directions: high-level semantic information from the encoder that contains complex structures such as rebar and void, and the other includes features reflecting the gradual recovery process from the generator. This design is intended to simultaneously process and integrate features from both the encoder and the generator, helping the network more accurately identify and eliminate rebar clutter while preserving the integrity of critical structures like voids during reconstruction. Channel attention primarily focuses on weighting feature channels. Some channels might be better at capturing rebar reflection signals while others may be more sensitive to voids or other structural features. This module learns the weights for each channel, identifying the most critical channel features for the current task. High-weight channels indicate that their features are particularly important for distinguishing rebar and non-rebar regions. By assigning higher weights to important channels, their feature representation is enhanced, allowing these crucial details to be better restored and utilized in subsequent decoding processes. In contrast, spatial attention focuses on the spatial positions, weighting these positions to highlight areas more important for the task. This means the model can distinguish between and focus on specific spatial regions containing rebar signals. By assessing the importance of each position in the image, it highlights regions containing important information such as voids. Spatially weighted regions receive more attention during feature extraction and image recovery stages, ensuring that the features in these areas are effectively preserved and enhanced in the final output image. The structure of the CSA module is depicted in Figure 3a. The network begins with the channel attention module, as shown in Figure 3c. First, the input features  
Figure 2: The structure of the encoder–generator and discriminator. (**a**) Encoder–generator. (**b**) Discriminator. and \(F_{2}\) are subjected to average pooling and max pooling operations, respectively. Max pooling emphasizes key features in the image, while average pooling helps retain more comprehensive background information. The pooled features are then concatenated to integrate information from both inputs, generating two new feature vectors from the average and max pooled features. These vectors are then passed through a shared layer (Shared MLP), which first compresses the number of channels to \(1/4\) of the original size, applies a ReLU activation function, and then expands them back to the original channel size. The two resulting outputs use element-wise summation, and then weight \(M_{C}\) for each channel is output via a Sigmoid activation function. These weights are then element-wise multiplied with \(F_{2}\) to produce \(F_{2}^{\prime}\), achieving weighted channel features for the input feature. The weighted features \(F_{2}^{\prime}\) and the original generator input features \(F_{1}\) are fed into the spatial attention module for further spatial-level weighting, as shown in Figure 3b.