## 2 Method
### Residual Dense Attention Block (RDAB)  
The RDABs, illustrated in Figure 1b, are designed to enhance feature extraction by combining densely connected convolutional layers, local feature fusion, local residual learning, and attention mechanisms. This architecture promotes information flow, encourages feature reuse, and improves noise suppression efficiency. A sequential memory mechanism is employed to propagate feature information between RDABs. This design maintains a continuous flow of learned features, facilitates effective gradient propagation, reduces the risk of vanishing gradients, and enhances the overall utilization of extracted information for noise removal. Each RDAB adopts a growth-rate-based feature expansion strategy, which concatenates intermediate feature representations at different depths. The block comprises dilated convolutional layers with a 3 \(\times\) 3 kernel, batch normalization (BN) [38], and ReLU activation functions to capture both fine and broad details. Additionally, spatial and channel attention mechanisms are incorporated at the end of each RDAB to guide the network's focus toward informative spatial and channel features, particularly in regions heavily corrupted by noise. By combining outputs from different depths and emphasizing relevant features through attention, RDABs preserve both shallow and deep structural details crucial for accurate phase restoration. Within each RDAB, let I\({}_{\text{d}-1}\) and I\({}_{\text{d}}\) represent the input and output feature maps, respectively. The output of the n-th convolutional layer inside the RDAB is:  
\[\text{I}_{\text{d,n}}=\sigma(\text{W}_{\text{d,n}}[\text{I}_{\text{d}-1},\text{ I}_{\text{d,1}},\ldots,\text{I}_{\text{d,n}-1}]) \tag{6}\]  
where \(\sigma\) is the ReLU activation and W\({}_{\text{d,n}}\) represents the convolutional weights. Here, each convolution layer produces G new feature maps. To manage feature growth, local feature fusion is performed via a 1 \(\times\) 1 convolution layer as:  
\[\text{I}_{\text{d,LF}}=\text{H}_{\text{LFF}}([\text{I}_{\text{d}-1},\text{I}_{ \text{d,1}},\ldots,\text{I}_{\text{d,N}}]). \tag{7}\]  
Following fusion, local residual learning is performed by adding the fused output back to the input of the RDAB:  
\[\text{I}_{\text{A}}=\text{I}_{\text{d}-1}+\text{I}_{\text{d,LF}}. \tag{8}\]  
where I\({}_{\text{A}}\) denotes the intermediate output before attention enhancement. To further enhance feature learning, each RDAB incorporates channel and spatial attention mechanisms that guide the network to focus on informative features and critical spatial regions. The intermediate feature map I\({}_{\text{A}}\in\text{ R}^{c\times\text{h}\times\text{w}}\), where c denotes the number of channels and h and w represent the spatial height and width, respectively, undergoes channel and spatial attention enhancement. First, channel attention is computed by compressing the spatial dimensions through global average pooling, producing a vector I\({}_{\text{C}}\in\text{ R}^{c\times 1\times 1}\). This vector is passed through two fully connected (FC) layers to capture inter-channel dependencies as:  
\[\text{M}_{\text{c}}(\text{I}_{\text{A}})=\text{BN}(\text{W}_{1}(\text{ ReLU}(\text{W}_{0}(\text{AvgPool}(\text{I}_{\text{A}}))+\text{b}_{0}))+\text{b}_{1}). \tag{9}\]where \(\mathrm{W_{0}}\in\mathrm{R^{(c/r)\times c}}\), \(\mathrm{b_{0}}\in\mathrm{R^{c/r}}\), \(\mathrm{W_{1}}\in\mathrm{R^{c\times(c/r)}}\), \(\mathrm{b_{1}}\in\mathrm{R^{c}}\), and r is the reduction ratio. BN denotes batch normalization. Next, spatial attention is applied using a \(1\times 1\) convolution, followed by two \(3\times 3\) dilated convolutions to capture contextual information. A final \(1\times 1\) convolution and batch normalization are applied to generate the spatial attention map:  
\[\mathrm{M_{s}(I_{A})}\mathrm{=BN(f_{3}^{1\times 1}(f_{2}^{3\times 3}(f_{1}^{3 \times 3}(f_{0}^{1\times 1}(I_{A}))))).} \tag{10}\]  
Here, \(\mathrm{f_{i}^{k\times k}}\) denotes convolutional layers with specified filter sizes.