## 1 Introduction  
High Dynamic Range (HDR) imaging represents the capability to capture and display a wide range of brightness levels within a single image, faithfully replicating the range and detail discernible to the human eye in the natural world. In contrast, conventional Low Dynamic Range (LDR) images compress the representation of reality's colors, making it challenging to preserve details in extreme brightness and darkness. The increasing demand for HDR images is driven by applications such as photography, movies, gaming, virtual reality, real-time surveillance, and security, where HDR images can accurately reflect real scenes with high quality. However, the specialized and costly HDR cameras are impractical for regular users, who typically capture images with a low dynamic range [1]. Therefore, it is crucial to utilize HDR image reconstruction algorithms to reconstruct HDR images from LDR images. HDR image reconstruction algorithms can be further classified into two main categories: multiexposure HDR image reconstruction and single-exposure HDR image reconstruction. Multiexposure HDR image construction algorithms aim to reconstruct HDR content by aligning and fusing multiple LDR images captured at different exposure times of the same scene [2; 3; 4; 5; 6; 7; 8; 9; 10]. However, the complexity of capturing images with varied exposure times in the same scene limits the algorithms' generalization capabilities. Single-exposure HDR reconstruction algorithms reconstruct HDR content from a single LDR image. Since only a single image is used as input, there is a serious lack of information in the overexposed/underexposed regions. Therefore, recovering the missing details in the overexposed/underexposed regions is the most important and difficult point in the single-exposure HDR image reconstruction task. In addition, the reconstruction task also involves dequantization, linearization, and suppression of compression artifacts to enhance the visual effect by expanding the image's dynamic range. To overcome the challenge of missing information, some traditional model-based single-exposure HDR image reconstruction algorithms directly improve the brightness or enhance the contrast of the input [11; 12]. However, these model-based methods make it almost impossible to infer the missing contents in LDR images. A number of techniques utilize image local heuristics to extend the dynamic range [13; 14], but they tend to overlook the detailed information in the reconstructed image. To address multiple subtasks in the single-exposure HDR image reconstruction task, data-driven algorithms for single-exposure HDR image reconstruction have been proposed [15; 16; 17; 18; 19; 20; 21; 22; 23]. However, these algorithms are inadequate in recovering fine details in reconstructed HDR images, as shown in Figure 1. Furthermore, they do not sufficiently consider the division of subtasks within HDR image reconstruction based on the LDR imaging pipeline principle, resulting in the problem of artifacts and tonal loss in HDR images while recovering the lost information, and failing to maintain a good dynamic range. To address the above problem, this work introduces the Reversing Lossy UNet (RLUNet) model, which aims to effectively balance dynamic range expansion while recovering lost content in overexposed regions by leveraging a deeper understanding of LDR image pipeline principles. The proposed model is implemented based on the LDR-HDR framework, incorporating a Texture Filling Module (TFM) block designed in conjunction with the Recovery of Overexposed Regions (ROR) module to enhance the recovery of lost details in overexposed areas. The LDR-HDR framework is proposed by reversing the operations involved in lossy image operations, grounded in the principles of the LDR imaging pipeline. This framework divides the imaging process of reconstructing HDR images from LDR images into exposure adjustment, denoising, recovery of overexposed regions, dequantization, linearized mappings, and suppression of compression artifacts.