## 2 Related Work
### Single-Exposure HDR Image Reconstruction  
The traditional approaches to single-exposure HDR image reconstruction rely on the image's internal features to predict the scene's brightness. For instance, [PERSON] et al. [26] estimated the light source density to expand the dynamic range, [PERSON] et al. [27] applied a cross-bilateral filter to enhance the input LDR image, and [PERSON] et al. [12] utilized a global operator to approximate tonal expansion to improve visual quality. In recent years, due to the Convolutional Neural Network's (CNN) outstanding performance in tasks such as denoising, image inpainting, coloring, and compression artifact restoration, many researchers have begun to explore the use of CNN in single-exposure HDR image reconstruction tasks. Single-exposure HDR image reconstruction can be roughly categorized into indirect generation, reverse camera imaging pipeline, and direct generation method. The indirect generation method involves generating different exposure levels from a single exposure image, which are then fused to reconstruct HDR images [28; 29; 30; 31; 32; 33; 34; 35]. However, this method requires multiple forward computations and extended processing times, particularly in complex environments. The reverse camera imaging pipeline method employs multiple networks to learn the stages of the reverse camera imaging pipeline to generate HDR images. [PERSON] et al. [36] divided the HDR reconstruction task into three subtasks: dequantization, linearization, and hallucination. They developed separate networks for each subtask and then fine-tuned the entire model together to enhance its ability to handle the input image. Building on this work, [PERSON] et al. [37] further improved the process by fine-tuning the networks using unpaired datasets. To handle the linearization task, they utilized two symmetric networks to model the camera response function and the inverse camera response function. While this approach yielded relatively good reconstruction performance, it also necessitated a large number of parameters, posing challenges for generalization and training. The direct generation method primarily focuses on subproblem-oriented approaches. It involves dividing the task of reconstructing an HDR image from an LDR image into several subproblems, and a neural network with modules designed to solve these subproblems directly reconstructs the content of the HDR image from an LDR image. Most early methods addressed the issue of missing information in overexposed image regions due to severe information loss in the LDR image. [PERSON] et al. [38] were the first to propose the HDRCNN network. In this network, the encoder operates directly on the input LDR image, while the decoder works in the logarithmic domain to generate HDR data. [PERSON] et al. [39] introduced a feature masking mechanism based on [38]. This mechanism involves multiplying the feature mapping of each layer by a corresponding soft mask and proposing Visual Geometry Group (VGG) perception loss to assist in reconstructing good texture effects. [PERSON] et al. [40] utilized Generative Adversarial Network (GAN) networks, incorporating a hybrid loss comprising reconstruction, adversarial, and perceptual losses to achieve good performance. [PERSON] et al. [41] employed the feedback mechanism in their FHDR network, which consists of a feature extraction block, feedback block, and HDR reconstruction block. They also utilized a global residual skip connection to guide the HDR reconstruction block in the final layer. Due to the instability of GAN network training and the difficulty of adjusting the parameters of the RNN feedback mechanism, current research is exploring the use of CNN networks to divide HDR reconstruction into distinct tasks and address them individually. [PERSON] et al. [42] proposed a multiscale architecture called ExpandNet, which consists of three branches.