## 2 Related Work
### Single-Exposure HDR Image Reconstruction
These branches individually learn to maintain and expand high-frequency detail, information about larger pixel neighborhoods, and the input global context to provide overall information. [PERSON] et al. [43] divided the HDR image reconstruction task into two stages. The first stage involves basic enhancement tasks such as exposure correction and denoising, while the second stage focuses on learning tone mapping and bit expansion tasks. They employed different numbers of Residual Dense Attention Blocks (RDABs) to accomplish these tasks, which combine Residual Dense Blocks and Spatial Attention Modules. Subsequently, the subtask of denoising has gradually attracted the attention of more and more researchers. According to imaging knowledge inspiration, [PERSON] et al. [44] divided the HDR imaging task into three subtasks: inferring the pixel value of the overexposed area, adjusting sensor gain and exposure time, and denoising the noise generated by the LDR image. [PERSON] et al. [23] proposed a spatial dynamic encoder-decoder network called HDRUNet, which focuses on denoising and dequantization. The network consists of a UNet-style base network to make full use of the hierarchical multiscale information, a conditioning network to perform pattern-specific modulation, and a weighting network for selectively retaining information. [PERSON] et al. [15] focused on denoising and detail reconstruction. They designed two branches based on the UNet network to learn brightness information and texture information, respectively. In order to obtain more detailed information, researchers have refocused their studies on the exploitation of overexposed and underexposed areas. [PERSON] et al. [16] introduced a Feature-Transformation U-shaped Network (FTUnet) to improve the reconstruction of overexposed regions by extracting spatially rich features through a self-attention mechanism. They also proposed an \"Excitation-Restoration\" (ER) subnetwork that engages the inter-channel attention mechanism to improve the color accuracy of the generated HDR images. [PERSON] et al. [17] proposed a joint framework and dataset combining color events and a single LDR image, which are also used to restore the content and color of HDR images. [PERSON] et al. [18] proposed a framework based on a conditional Denoising Diffusion Probabilistic Model (DDPM) to recover complex details from shadows and highlights. [PERSON] et al. [19] developed an exposure mask learning technique to adaptively separate over/under and well-illuminated regions for each scene. They also introduced a dual-intensity guidance method using the channel-variant attribute of RAW Image Format (Raw) images and a global spatial guidance built on a transformer with spatial attention. [PERSON] et al. [20] addressed the overexposure/underexposure problem of LDR images by introducing an encoder-decoder backbone network with a soft mask bootstrap module for predicting kernels for pixel-level decoupling. However, these algorithms are insufficient to recover fine details in reconstructed HDR images. Furthermore, they do not sufficiently consider the division of subtasks within HDR imaging based on the imaging pipeline principle. In this paper, an LDR-HDR framework is proposed to reverse the lossy image quality operations introduced based on the LDR imaging pipeline principle. According to the framework, the RLUNet model is proposed to reconstruct HDR images by recovering overexposed regions, dequantizing, linearizing mappings, and suppressing compression artifacts.