## 4 Experiments
### Comparison with State-of-the-Art Methods
However, HDRUNet [23] does not account for the distinction between conventional images and the specific challenges of HDR image reconstruction, leading to color distortion and an insufficient dynamic range in the generated results. In contrast, KUNet [44] produces results with color shifts and speckle distortion by reasoning based on imaging knowledge without a specific focus on the HDR imaging pipeline [16]. To ensure fair testing, Min-Max Normalization is employed as a replacement for Max Abs Normalization, and the metrics are reevaluated accordingly. As indicated in Table 2, our approach outperforms the current state-of-the-art (SOTA) model by 1.21 dB in PSNR-L and 1.81 dB in PSNR-N on the NTIRE dataset across various evaluation metrics. This is largely attributed to the benefits conferred by the Texture Filling Module (TFM) block and the Recovery of Overexposed Regions (ROR) module in the RLUNet. Benefiting from these modules, the RLUNet model effectively restores missing content information in overexposed regions and produces more detailed HDR images. Noticeably, CEVR [34] utilizes a single exposure to produce various multiexposure images. This results in the maximum pixel value of the generated HDR image being much higher than the maximum value of the truth image. Consequently, CEVR [34] performs poorly in all metrics except the HDR-VDP-2 metric. In Table 3, our method demonstrates superior performance across several evaluation metrics on the HDR-Real dataset. Compared to the SOTA model SingleHDR [36], our method offers a deeper understanding of LDR image processing principles, enabling effective balancing of dynamic range expansion and recovery of lost content in overexposed regions. **Qualitative Comparison:** For qualitative evaluation, the results of the reconstruction are presented in Figures 7 and 8. \begin{table}
\begin{tabular}{l c c c c c} \hline  & & \multicolumn{3}{c}{**HDR Image**} & \multicolumn{2}{c}{**Tone-Mapped HDR Images**} \\ \cline{3-6}
**Method** & **Venue** & **MSE \(\downarrow\)** & **HDR-VDP-2 \(\uparrow\)** & **PSNR-N \(\uparrow\)** & **SSIM-N \(\uparrow\)** \\ \hline HDRCNN [38] & TOG17 & 0.0062 & 49.92 & 56.69 & 0.9838 \\ ExpandNet [42] & CGF18 & 0.0370 & 47.80 & 55.43 & 0.9761 \\ FHDR [41] & GlobalSIP19 & 0.0198 & 48.40 & 54.72 & 0.9721 \\ DeepHDR [39] & TOG20 & 0.0312 & 48.00 & 54.94 & 0.9723 \\ SingleHDR [36] & CVPR20 & 0.0046 & 50.55 & 58.41 & 0.9888 \\ HDRUNet [23] & CVPRW21 & 0.0273 & 47.41 & 53.76 & 0.9659 \\ KUNet [44] & IJCAI22 & 0.0407 & 46.15 & 53.66 & 0.9637 \\ CEVR [34] & ICCV23 & 0.0547 & 48.72 & 54.98 & 0.9734 \\ RLUNet & ours & **0.0044** & **50.71** & **58.50** & **0.9893** \\ \hline \end{tabular}
\end{table} 
Table 3: Quantitative comparisons of the state-of-the-art methods for HDR image reconstruction on the HDR-Real testset. The MSE and HDR-VDP-2 are computed on HDR images, and PSNR and SSIM are computed on tone-mapped HDR images, respectively. PSNR-N and SSIM-N represent images using Min-Max Normalization. The best results are represented in **bold**, and the second-best performance is denoted by underlined text. Here, \(\uparrow\) indicates that a higher score represents better quality, while \(\downarrow\) indicates that a lower score corresponds to better reconstruction. In the NTIRE dataset, the red box zooms in on the details of the overexposed region, and the green box zooms in on the details of the underexposed region. In Figure 7, our method demonstrates superior results in recovering the content information of overexposed regions. Not only does it show the more complete structural information, but the recovered detailed content also outperforms the current state-of-the-art model. In the underexposed region, RLUNet restores the original color and brightness information of the image through Dequantization (DQ), producing results that are closer to ground truth in visual perception. Figure 7: Qualitative results of our method comparison with state-of-the-art methods on the NTIRE testset [24].