# A Nested UNet Based on Multi-Scale Feature Extraction for Mixed Gaussian-Impulse Removal
## 4 MSNUNet
### Mscam  
In recent years, CNNs have gained wide adoption in image processing due to their remarkable performance. Conv serves as a fundamental building block of CNNs and typically includes Conv, BN, and ReLU functions. However, the standard Conv has inherent limitations in extracting exclusively local features [31], which poses a significant disadvantage for image processing. To overcome this limitation, MSUNNet utilizes MSU-Subnet to extract multi-scale features that are more diverse than those obtained through a standard Conv. These features consist of high-resolution feature maps with precise spatial information and low-resolution feature maps with reliable semantic information. To effectively integrate these rich features, we follow the component arrangement described in [32] and propose a new module called MSCAM, depicted in Figure 2b. MSCAM consists of two steps.  
In step 1 of MSCAM, we apply layer normalization (LN) to normalize the input feature maps. The normalized feature maps are then processed using a \(1\times 1\) Conv, followed by a \(3\times 3\) depth-wise Conv (DConv). The DConv offers greater efficiency compared to traditional Conv as it has fewer parameters and lower computational costs. The Gaussian error linear unit (GELU) activation function is employed to implement the non-linear mapping relationship. The resulting feature maps are fed into the CAB, which captures the correlation between global feature channels. The internal structure of the CAB is illustrated in Figure 3. Finally, a \(1\times 1\) Conv is utilized to aggregate these features and output them to the second step of the module.  
Figure 3: CAB internal structure.  
Figure 2: MSUNNet architecture for mixed noise removal.  
The CAB implementation can be represented by the following equations [29], where the input is denoted as \(c\) and the output is denoted as \(c_{3}\). These calculations are shown in Equations (7)-(9),  
\[c_{1}=FC_{1}[g(c)] \tag{7}\]  
\[c_{2}=FC_{2}[ReLU(c_{1})] \tag{8}\]  
\[c_{3}=c*Sigmoid(c_{2}) \tag{9}\]  
where the function \(g\) in Equation (7) represents the global average pooling. The input feature map, \(c\), undergoes global average pooling followed by a fully connected layer, \(FC_{1}\), to obtain \(c_{1}\). Subsequently, \(c_{1}\) is passed through the ReLU activation function and another fully connected layer, \(FC_{2}\), to obtain the compressed image features, \(c_{2}\). The Sigmoid function is then applied to \(c_{2}\), yielding weight coefficients between channels. Finally, these weight coefficients are multiplied with the input \(c\) to obtain the final result.  
In Step 2, we employ a \(1\times 1\) Conv to enable the interaction of information among the diverse features acquired from the previous stage.  
The incorporation of the CAB in MSCAM enhances the network's focus on important features [33], leading to the improved utilization and preservation of image texture details. Furthermore, residual connections are introduced to enhance the network's performance by aiding in the reconstruction of neglected high-frequency feature information.