## 3 Methodology
### Network Structure  
The effectiveness of the UNet-like encoder-decoder architecture in reconstructing HDR images has been demonstrated by many algorithms [23, 38, 44]. This architecture can effectively use hierarchical multiscale information from low-level to high-level features. Therefore, based on the LDR-HDR framework, the UNet-like network architecture is chosen as the backbone for the Reverse Lossy Network design. The network framework is shown in Figure 4.  
**Network construction:** The Reverse Lossy UNet(RLUNet) model consists of a Reverse Lossy Network and a Luminance Guidance Module(LGM) that utilizes a Texture Filling Module (TFM) block. The Reverse Lossy Network initially extracts features using 3 \(\times\) 3 convolutional layers and ReLU activation functions to map the content of the LDR image into a rich feature space. This feature is then used for the subsequent conversion of LDR to HDR features. This process involves transforming the dense rich features into a relatively loose feature space, which can be represented as:  
\[I_{int}=(ReLU\circ conv_{3\times 3}\circ conv_{3\times 3}\circ ReLU\circ conv_{3 \times 3})(I_{L}), \tag{1}\]  
where \(I_{L}\) represents the input LDR image and \(I_{in}\) represents the features entered into the subsequent module. After extracting the features of the LDR image, four Imaging Pipeline Module (IPM) blocks are used to convert the LDR features into HDR features. The IPM blocks include the Recovery of Overexposed Regions (ROR), Dequantization (DQ), and Reconstruction of Linearized Mappings (RLM). To fully explore global information, two smaller IPM blocks,  
Figure 4: The overall structure of the proposed Reverse Lossy UNet (RLUNet). Figure 3: Reconstruction of HDR images from LDR images is typically divided into several subtasks. The RLUNet model is specifically designed to reconstruct HDR images in terms of Recovering Overexposed Regions (ROR), Dequantization (DQ), Reconstruction of Linearized Mappings (RLM), and Compression Artifact Adjustment Module (CAAM), which focuses on reversing operations that cause a loss of image quality. which operate at a reduced scale and with increased channel capacity, are integrated into the UNet structure. Meanwhile, following TFM block processing on the input LDR image, a valid mask map is computed by the LGM. This mask map serves as prior information for the IPM block to adjust the modules in the IPM block. During the IPM block reconstruction process, the Compression Artifact Adjustment Module (CAAM) block is utilized to address compression artifacts and assist in adjusting the input features of the IPM block. Finally, the HDR features from the fourth IPM block and the features from the LDR image are fused as input to the CAAM block for the final HDR features. The formula is expressed as follows:  
\[I_{out}=fuse(I_{cnam},I_{ipm}), \tag{2}\]  
where \(I_{cnam}\) represents the CAAM block's output features, \(I_{ipm}\) represents the IPM block's output features, and \(I_{out}\) represents the final reconstructed HDR features. The \(fuse(\cdot)\) function denotes the fusion operation of two features. In comparison to more complex fusion methods, such as splicing or multiplication, the pixel-by-pixel addition operation not only exhibits a low computational complexity but also effectively stabilizes the gradient propagation process. At last, the reconstructed rich features are mapped to the three-channel HDR image with compact information using multiple convolutional layers. This can be expressed as follows:  
\[I_{H}=(conv_{3\times 3}\circ ReLU\circ conv_{3\times 3}\circ conv_{3\times 3})(I _{out}), \tag{3}\]  
where \(I_{H}\) represents the output HDR image. In the following, we will introduce the main parts of the RLUNet model separately.