## 5 Conclusions  
In this paper, the Reversing Lossy UNet (RLUNet) model is introduced to balance the effects of dynamic range expansion and recovery of lost information in overexposed regions by leveraging a deeper understanding of LDR image pipeline principles. The model is  
Figure 10: Reconstruction results of our RLUNet algorithm in comparison with state-of-the-art algorithms on a dataset with real-world images. The first and the last images are the LDR input and our RLUNet result image. The remaining images are the result images from existing methods, HDRCNN [38], ExpandNet [42], FHDR [41], DeepHDR [39], SingleHDR [36], HDRUNet [23], KUNet [44] and CEVR [34], respectively. implemented within 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 visual performance and detail of the overexposed areas in the reconstructed HDR image. The LDR-HDR framework is designed by reversing the lossy image operations. Lossy image operations are introduced based on the LDR imaging principle. Additionally, this framework divides the imaging process of reconstructing an HDR image from an LDR image into exposure adjustment, denoising, recovery of overexposed regions, dequantization, reconstruction of linearized mapping, and suppression of compression artifacts. The RLUNet model features the Reverse Lossy Network, which includes four Image Pipeline Module (IPM) blocks and three Compression Artifact Adjustment Module (CAAM) blocks designed to reconstruct HDR content based on the proposed LDR-HDR framework. The IPM block consists of three parts: Recovery of Overexposed Regions (ROR), Dequantization (DQ), and Reconstruction of Linearized Mappings (RLM), which are responsible for the recovery of overexposed areas, dequantization, and linearized mapping, respectively. The Compression Artifact Adjustment Module (CAAM) block is responsible for the suppression of compression artifacts. A Texture Filling Module (TFM) block has been integrated to support the Luminance Guidance Module (LGM) in determining both invalid and valid masks. The invalid mask directs the partial convolution process in the Recovery of Overexposed Regions (ROR) model, thereby enhancing the visual quality and detail texture of the overexposed areas in the reconstructed HDR image. Experiments demonstrate that the metric performance and subjective effect of the HDR images reconstructed by the proposed RLUNet model outperform the state-of-the-art methods. However, our approach still has some limitations. The more advanced and complex camera pipeline, as emphasized by [74; 75], suggests that operations such as white balance, color space conversion, and gamut mapping are also necessary when mapping RAW Image Format (RAW) images to Joint Photographic Experts Group (JPEG) images. Since these operations primarily adjust and optimize color information, which is analogous to the role of nonlinearization, this paper does not further design to invert these operations. It is possible that these fine color conversion operations may result in a reconstructed image that differs slightly in color from the ground truth image. Furthermore, our network is not sufficiently lightweight for consumer and industrial environments, and the available dataset is small, necessitating further optimization. Not applicable. For the NTIRE dataset, register a codalab account and log in, then find the download link on this page: [[https://competitions.codalab.org/competitions/28161#participate-get-data](https://competitions.codalab.org/competitions/28161#participate-get-data)]([https://competitions.codalab.org/competitions/28161#participate-get-data](https://competitions.codalab.org/competitions/28161#participate-get-data)) (accessed on 10 April 2023).