## 4 Experiments
### Comparison with State-of-the-Art Methods  
To evaluate the performance of the proposed RLUNet, comparisons are made with eight existing state-of-the-art methods for learning-based single-image HDR reconstruction: HDRCNN [38], ExpandNet [42], FHDR [41], DeepHDR [39], SingleHDR [36], HDRUNet [23], KUNet [44], and CEVR [34]. Most of these methods utilize different datasets that are not publicly available, and these datasets involve special operations on the data to be processed. However, HDRUNet [23] and KUNet [44] use the same training dataset as the proposed RLUNet method. The images in the NTIRE testset were randomly cropped to a size of \(1024\times 1024\), considering that some methods can only test images with a resolution that is a multiple of 32. The images in the datasets are stored with gamma correction and are nonlinear. Before the quantitative evaluation, the test results in the experiments are uniformly gamma-corrected. **Quantitative Comparison:** Tables 2 and 3 show the performance of the different methods on the NTIRE and HDR-Real datasets. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Category** & **Parameter** & **Value/Configuration** \\ \hline \multirow{4}{*}{Convolution Setting} & Kernel size & \(3\times 3\), \(1\times 1\) \\ \cline{2-3}  & Stride & 1 (default), 2 (down) \\ \cline{2-3}  & Padding & 1 \\ \hline \multirow{2}{*}{Feature Channels} & Regular block & 64 \\ \cline{2-3}  & IPM blocks & [128; 256; 256; 128] \\ \hline \multirow{2}{*}{Activation Function} & IPM Blocks & LeakyReLU \\ \cline{2-3}  & Other Layers & ReLU \\ \hline \multirow{4}{*}{Optimizer and Hyperparameters} & Optimizer & ADAM \\ \cline{2-3}  & Learning Rate & \(2\times 10^{-4}\) \\ \cline{2-3}  & Decay rate & Decay by a factor of 2 every \\ \cline{2-3}  & & 1130 epochs \\ \hline \multirow{2}{*}{Training Configuration} & Total epochs & 5650 \\ \cline{2-3}  & Batch Size & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Implementation details. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{4}{c}{**NTIRE (Max Abs)**} & \multicolumn{2}{c}{**NTIRE (Min-Max)**} \\ \cline{2-7}
**Method** & **PSNR-L** & **PSNR-\(\mu\)** & **SSIM-L** & **HDR-VDP-2** & **PSNR-N** & **SSIM-N** \\ \hline HDRCNN [38] & 25.50 & 26.05 & 0.6849 & 52.01 & 27.49 & 0.4071 \\ ExpandNet [42] & 25.77 & 20.68 & 0.3261 & 49.50 & 25.15 & 0.6125 \\ FHDR [41] & 26.11 & 22.93 & 0.3889 & 50.65 & 29.00 & 0.7462 \\ DeepHDR [39] & 29.98 & 20.45 & 0.6784 & 49.79 & 18.96 & 0.6354 \\ SingleHDR [36] & 23.71 & 20.68 & 0.5573 & 49.22 & 25.74 & 0.3178 \\ HDRUNet [23] & 41.89 & 33.94 & 0.9949 & 57.39 & 32.10 & 0.8679 \\ KUNet [44] & 44.58 & 34.02 & 0.9952 & 57.36 & 38.44 & 0.9437 \\ CEVR [34] & — & 5.05 & — & 51.31 & 25.74 & 0.3179 \\ RLUNet (our) & **45.79** & **34.16** & **0.9957** & **57.56** & **40.2579** & **0.9600** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Quantitative comparisons of the state-of-the-art methods for HDR image reconstruction on the NTIRE testset. NTIRE (Max Abs) refers to normalization to the peak of ground-truth HDR images, while NTIRE (Min-Max) refers to Min-Max Normalization. The best results are represented in **bold**, and the second-best performance is denoted by underlined text. The methods ExpandNet [42], FHDR [41], and DeepHDR [39] predict linear HDR values. Although gamma corrects their results, aligning the exposures remains difficult. Consequently, these methods show low performance in reference-based metrics. The HDRCNN [38] in the table directly learns to map from LDR to HDR, but it only handles a small range of overexposed regions. However, due to its masking strategy, it achieves higher metrics on both datasets compared to all other methods. SingleHDR [36] aims to restore relative brightness rather than absolute brightness, resulting in poor performance on the NTIRE testset. Lastly, the network structures of HDRUNet [23] and KUNet [44] are specifically designed for the NTIRE dataset, leading to significantly higher metrics on the NTIRE testset compared to other methods.