## 3 Methodology
### Network Structure
To provide a detailed illustration of the CAAM block's operation, we shall use the outermost CAAM block as an example. Specifically, The CAAM module first uses a modified residual convolution to feature map the input features \(I_{in}\) into \(\hat{I_{in}}\). This process can be expressed as follows:  
\[I_{in^{\prime}}=((ReLU\circ conv_{3\times 3}\circ ReLU\circ conv_{3\times 3})(I _{in}), \tag{6}\]  
\[\hat{I_{in}}=(ReLU\circ conv_{3\times 3}\circ ReLU\circ conv_{3\times 3})(I _{in^{\prime}}+I_{in}),. \tag{7}\]  
Subsequently, the mapped feature \(\hat{I_{in}}\) is passed into the Refine Module along with the bootstrap features, \(I_{p}\), generated by the IPM block. The Refine Module is outlined in Algorithm 1. In the Refine Module, the similarity between the input feature, \(\hat{I_{in}}\), and the guide feature, \(I_{p}\), is initially calculated using the similarity bootstrap mechanism. The original image \(I_{in}\) is then weighted and fine-tuned using \(1\times 1\) convolution. With this design, the CAAM block is capable of effectively mitigating compression artifacts in the image and enhancing the quality of the HDR reconstruction. This ensures a more accurate recovery of details and textures in different regions. Figure 5: The scheme of the proposed Texture Filling Module (TFM) block. ```
0: Extracted features \(\hat{I_{in}}\), guiding features \(I_{p}\), input \(I_{in}\), small parameter \(\varepsilon\)
0: Refined output: \(I_{canam}\)
1: Step 1: Calculate Similarity
2: Compute dot product: \(S=\hat{I_{in}}\cdot I_{p}\)
3: Compute normalization: \(N=\max(\|\hat{I_{in}}\|_{2}\cdot\|I_{p}\|_{2},\varepsilon)\)
4: Obtain similarity: \(Similarity(\hat{I_{in}},I_{p})=\frac{S}{N}\)
5: Step 2: Channel-wise Multiplication
6: Compute weighted input: \(W=Similarity(\hat{I_{in}},I_{p})\odot I_{in}\)
7: Step 3: Refine the Output
8: Apply \(1\times 1\) convolution: \(I_{canam}=conv_{1\times 1}(W)\)
9:return\(I_{canam}\)
```  
**Algorithm 1** Refine Module  
Imaging Pipeline Module (IPM): The composition of the Imaging Pipeline Module (IPM) block is shown in Figure 6.  
The nonlinearized CRF operates on the image globally, without requiring reversion between neighboring pixels [58; 66]. To tackle this challenge, an octave convolution [67] is utilized to decompose the LDR features into high-frequency and low-frequency components. For the high-frequency features, a series of five Residual Dense convolutional layers (RDconv) are employed to capture the global context of the extensive sensory field, thereby facilitating the Reconstruction of the Linearization Mappings (RLM) module. The inputs of each RDconv are derived by concatenating the outputs of all upper-layer RDconvs. The RDconv is shown as Equation (8):  
\[x_{i}=conv_{3\times 3}\Bigg{(}x+\sum_{x=1}^{i-1}x_{j}\Bigg{)},\quad i=0,1,2,3,4. \tag{8}\]  
where \(x_{1}\) to \(x_{5}\) are the outputs of the first five convolutional layers. Finally, \(x_{5}\) is extracted through a convolutional layer to obtain the feature \(I_{trlm}\) of the RLM module. For low-frequency features, long-range feature interdependencies are learned to recover information in overexposed regions. A mask computation strategy, inspired by [58], is adopted within the Luminance Guidance Module (LGM) to enhance the deep convo  
Figure 6: The composition of the Imaging Pipeline Module (IPM) block. lutional neural network's capability to recover overexposed regions, as illustrated in the following equation:  
\[ \begin{cases}Mask_{valid}(x)=\max(0,(x-t)/(1-t)),\\ Mask_{invalid}(x)=\max((x-1)/(t-1),1).\end{cases}  \tag{9}\]  
where \(x\) represents the pixel intensity of the TFM block's output in the range \([0,1]\). \(t=0.9\) is chosen as the threshold value for distinguishing overexposed regions and highlighted effective regions in LDR images. This threshold was chosen based on both empirical observation and theoretical considerations, as it effectively delineates the transition between the highlighted effective information and the saturated invalid information region.