## 3 Methodology
### Network Structure
**Texture Filling Module (TFM):** To enhance the visual quality of overexposed region reconstruction, we introduced the Texture Filling Module (TFM) to recover corrupted areas in the dataset, where LDR images often lack details in overexposed regions, making it challenging to generate effective intermediate prediction maps. Consequently, a single-stage TFM block was designed to tackle this issue. Existing restoration modules, which employ convolutional architectures, often lack a sufficiently large receptive field to capture the necessary detailed information for high-quality HDR image reconstruction. Even when a large receptive field is present, its limitations can still hinder the generation of sufficient detail. The TFM block, which utilizes Fast Fourier Convolution (FFC), addresses this issue by covering the entire image with its receptive field, thereby effectively preventing perceptual damage during the restoration process due to insufficient coverage. Furthermore, the mask generation strategy within the TFM block enhances the potential of high receptive fields, enabling the module to leverage these fields more effectively. The TFM block's masks exhibit better robustness than those of other restoration methods. Compared with other detail reconstruction modules, the TFM block effectively repairs the distorted content information of overexposed regions, and the generated masks contain richer detail information. The TFM block is primarily based on the Lama algorithm [65], which not only excels in restoring LDR images with significant overexposed regions but also extends its capabilities to high-resolution image restoration. In contrast, the computational power of most existing image coloring methods is restricted to images of \(256\times 256\) pixels, providing the Lama algorithm a significant advantage in our study. Initially, the mask processing is applied to the LDR images, and the corresponding formula is as follows:  
\[M= \begin{cases}1,&\text{if }\frac{1}{3}\sum_{c}I_{L}(x,y,c)>\tau,\\ 0,&\text{otherwise}.\end{cases}  \tag{4}\]  
where \(c\) represents the color channel index and \(\tau\) is 0.83. Given that the information surrounding the overexposed area is deemed to contain valid information in Equation (9), the selection of the highlighted and exposed area (pixel points with a value exceeding 0.83) as the fill area results in the recovery of a greater amount of texture detail. The above formula is utilized to fill the texture of the image where the pixel value exceeds the threshold. \[I_{tfm}=F(I_{L}*(1-M)\oplus M), \tag{5}\]  
where \(F\) denotes the TFM block's operation. Specifically, the scheme realized by the TFM block is shown in Figure 5. The resulting inpainting image \(I_{tfm}\) is then used as the input for the LGM. Compression Artifact Adjustment Module (CAAM): The common compression artifacts manifest differently in various parts of the image. For instance, blurry artifacts may be visible around the edges and details, while blocky artifacts are often found in overexposed areas. Therefore, to reduce compression artifacts and improve feature mapping, the Compression Artifact Adjustment Module (CAAM) block is used to condition the different features obtained in the network. The CAAM block extracts features preceding the hidden space through enhanced residual convolution and combines them with bootstrap features reconstructed by the Imaging Pipeline Module (IPM) block following the hidden space. These combined features are then input into the Refine module for adjustment to minimize compression artifacts. The inputs to the CAAM blocks are the incoming LDR features, the reconstructed features by the IPM block in the decoder, and the pre-hidden-space features, respectively. The inputs to the CAAM block are the incoming LDR features, the IPM reconstructed features in the decoder, and the pre-hidden-space features, while the bootstrap features correspond to the HDR features reconstructed in the decoder, the pre-IPM block reconstructed features in the encoder, and the post-hidden-space features.