## 4 Experiments
### Quantitative Results  
The approaches used in the comparative analysis include the traditional Patch-Based method [26], the pioneering DHDRNet model [10], [PERSON] et al. 's model [49], a state-of-the-art Transformer-based model aiming at low computational cost, the HDR-GAN [16] used as a basis, and the proposed model. The metrics of the Patch-Based, DHDRNet, Lopez-Cabrejos et al., and HDR-GAN models come from the reference papers [10; 16; 18], trained using the Kalantari dataset, with the exception of Patch-Based, which does not undergo training. Regarding the number of FLOPs and the number of parameters, all values were calculated. The performance evaluation of the models using the Kalantari dataset considers the quality metrics PSNR-\(\mu\) and SSIM-\(\mu\), as well as FLOPS, number of parameters, model size, inference time, and GPU memory used, which indicate the computational cost and complexity of the architecture, presented in Table 1. The results demonstrate that the proposed model offers an excellent balance between performance and computational efficiency. Although the [PERSON] et al. model achieves the best values in PSNR-\(\mu\) of 44.10 and SSIM-\(\mu\) of 0.9924, our solution presents a competitive performance of 43.51 for PSNR-\(\mu\) and 0.9917 for SSIM-\(\mu\), with significant advantages in terms of computational cost, presenting a lower number of FLOPs of 0.232 T, a reduced size of 1.45 MB, a faster inference time of 0.15 s, and an efficient GPU memory consumption using a maximum of 4.5 GB. Compared to HDR-GAN, the proposed model reduces FLOPs by 6.3\(\times\), parameters by 7\(\times\), size by 7.5\(\times\), and GPU memory by half, highlighting the effectiveness of depthwise separable convolutions for a lightweight and efficient architecture ideal for resource-constrained applications. In order to carry out a complete comparative analysis of the performance of the proposed model, calculations were performed with the other quality metrics, and the results are presented in Table 2. The results demonstrate that the proposed model, with seven times fewer parameters than HDR-GAN, maintains a competitive performance, achieving the best value in the SSIM-\(l\) metric with 0.9887 and ranking second in the PSNR-\(PU\) metrics with 43.17, SSIM-\(PU\) with 0.9902, and HDR-VDP3 with 9.24. Although the [PERSON] et al. model presents superior performance in PSNR-\(l\) with 42.07 and HDR-VDP2 with 67.79, its evaluation being limited to only three metrics restricts the comparative analysis. Regarding HDR-GAN, the proposal shows comparable efficiency in the reconstruction of HDR images, with minimum differences of 0.03 in PSNR-\(PU\) and 0.0011 in SSIM-\(PU\), consolidating itself as a lighter alternative that preserves visual quality, in addition to outperforming all models in the SSIM-\(l\) criterion. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**PSNR-\(\mu\uparrow\)**} & \multirow{2}{*}{**SSIM-\(\mu\uparrow\)**} & \multicolumn{3}{c}{\(\mathbf{N}^{\circ}\)**of**} & \multirow{2}{*}{**Size (MB) \(\downarrow\)**} & \multirow{2}{*}{\
\begin{tabular}{c} **Inference** \\ **Memory** \\ **(GB)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **GPU** \\ **Memory** \\ **(MB)** \\ \end{tabular} } \\ \hline Patch-Based & 40.80 & 0.9808 & - & - & - & - & - \\ DHDRNet & 42.67 & 0.9888 & - & 0.383 & - & - & - \\ HDR-GAN & 43.92 & 0.9905 & 1.455 & 2.557 & 10.9 & 0.19 & 8.3 \\ Lopez-Cabrejos & **44.10** & **0.9924** & 0.440 & **0.29** & 7.9 & 2.19 & 11.6 \\ Proposed Model & 43.51 & 0.9917 & **0.232** & 0.364 & **1.45** & **0.15** & **4.5** \\ \hline \hline \end{tabular}  
* The symbols \(\uparrow\) and \(\downarrow\) indicate that higher or lower values are preferable, respectively. **Bold** denotes the best result, while underline indicates the second-best result. The input size used for FLOPs, inference time, and GPU memory calculation is 1500 \(\times\) 1000.  
\end{table}