## 3 Proposed Method
### Two-Path Information Distillation Block (IDB)  
Figure 2 shows the designed two-path IDB that consists of two parts to take advantage of short- and long-path features, respectively. This two-path network differs from the information distillation network (IDN) model because it is constructed in every part and different paths use different convolutional kernels. Therefore, our algorithm can adaptively measure the features of the short and long paths at different scales.  
As shown in Figure 2, the inputs of the first part in the \(t\)-th iteration are \(F_{t-1}^{out}\) and \(F_{t}^{in}\). This paper obtains the following fusion:  
\[F_{t}^{LF}=H_{LF}\Big{(}\Big{[}F_{t-1}^{out},F_{t}^{in}\Big{]}\Big{)}, \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Kernel** & **Stride** & **Padding** \\ \hline Conv 1 & \(3\times 3\) & 1 & 1 \\ Conv 2 & \(5\times 5\) & 1 & 2 \\ Conv 3 & \(3\times 3\) & 1 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Structure of signal shallow feature extraction.  
Figure 2: Two-path information distillation block (IDB).  
where \(\left[F_{t-1}^{out},F_{t}^{in}\right]\) denotes the concatenation of \(F_{t-1}^{out}\) and \(F_{t}^{in}\). \(H_{LF}\) is a \(1\times 1\) convolutional layer composite function. So, the output \(O_{P1}\) of the first part is  
\[O_{P1}=\sigma\Big{(}Y_{1\times 1}^{1}\Big{(}\Big{[}\sigma\Big{(}Y_{3\times 3}^{ 2}\Big{(}F_{t}^{LF}\Big{)}\Big{)}+\sigma\Big{(}Y_{3\times 5}^{3}\Big{(}F_{t}^{LF} \Big{)}\Big{)}\Big{]}\Big{)}\Big{)}, \tag{6}\]  
where \(\sigma\) denotes the ReLU function [46]; \(Y_{1\times 1}^{1}\), \(Y_{3\times 3}^{2}\), and \(Y_{5\times 5}^{3}\) are the functions of \(1\times 1\), \(3\times 3\), and \(5\times 5\) convolutional layers in \(O_{P1}\), respectively; and \([\cdot]\) stands for the concatenation of the feature maps by various convolutional kernels. Then, the concatenation of the 64-dimensional (64D) feature maps of \(O_{P1}\) and the input \(F_{t}^{LF}\) are conducted in the channel dimension as Equation (7):  
\[R=\Big{[}S(O_{P1},64),F_{t}^{LF}\Big{]}, \tag{7}\]  
where \(S\) represents the slicing operation. As shown in Figure 2, except for the feature fusion layer, which employs 128 filters, all other convolutional layers are designed with 64 filters. Thus, we extract 64-dimensional feature maps from the 128-dimensional features generated by feature fusion. By combining the current 64-dimensional features with the previous \(F_{t}^{LF}\), this process can be viewed as retaining short-path (SP) information. Subsequently, the remaining 64-dimensional features are used as the input for the second part to extract long-path (LP) information:  
\[O_{P2}=\sigma\Big{(}Y_{1\times 1}^{4}\Big{(}\Big{[}\sigma\Big{(}Y_{3\times 3} ^{5}(O_{P1},64)\Big{)}+\sigma\Big{(}Y_{5\times 5}^{6}(O_{P1},64)\Big{)}\Big{]} \Big{)}\Big{)}, \tag{8}\]  
where \(Y_{1\times 1}^{4}\), \(Y_{3\times 3}^{5}\), and \(Y_{5\times 5}^{6}\) are the functions of \(1\times 1\), \(3\times 3\), and \(5\times 5\) convolutional layers in the second part, respectively. In the last, this paper expresses the aggregation of the input information, the SP information, and LP information by  
\[F_{t}^{out}=R+O_{P2}. \tag{9}\]  
For the \(1\times 1\) convolution kernel, it is only \(1\times 1\) in size, so it does not need to consider the relationship between pixels and surrounding pixels. It is mainly used to adjust the number of channels, linearly combining pixel points in different channels. Then, non-linear operations are performed, thus completing the functions of dimension increase and dimension decrease.