## 4 Experiments
### Quantitative Results

Table 1: Comparison of quality in relation to computational cost of models trained with Kalantari dataset. To analyze the generalization capability of the proposed model in relation to HDR-GAN, an analysis was performed using the IL-NIQE metric [48] calculated from the image in the tone-mapped domain. The comparisons made with this metric use the dynamic images from the Kalantari test set [10] and a selection of thirteen high-resolution static images from HDR-Eye [25], selected by the light condition, using only three exposures (EV \(-\)2, EV 0, and EV +2). Table 3 presents the results obtained from the Kalantari test set. It can be seen that the proposed model has the ability to reconstruct samples, obtaining values identical to the HDR-GAN model with IL-NIQE of 20.07. Thus, in the specific context of training with images characterized by movement and a resolution of \(1500\times 1000\), and considering the reduction in computational cost and number of parameters, the proposed model achieved excellent performance. For the selected images from the HDR-Eye set [25], the results calculated in terms of IL-NIQE are presented in Table 4. It can be seen that the proposed model outperformed the HDR-GAN model, reaching an IL-NIQE value of 24.52, demonstrating the ability to generalize and merge static images. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Model** & **PSNR-\(l\)** & **SSIM-\(l\)** & **PSNR-\(PU\)** & **SSIM-\(PU\)** & **HDR-VDP2** & **HDR-VDP3** \\ \hline Patch-Based & 38.11 & 0.9721 & 40.47 & 0.9775 & 59.38 & - \\ DHDRNet & 41.23 & 0.9846 & 41.83 & 0.9832 & 65.05 & - \\ HDR-GAN & 41.57 & 0.9865 & **43.20** & **0.9913** & 65.45 & **9.32 \({}^{*}\)** \\ Lopez-Cabrejos & **42.07** & 0.9886 & - & - & **67.79** & - \\ Proposed Model & 40.88 & **0.9887** & 43.17 & 0.9902 & 65.32 & 9.24 \\ \hline \end{tabular} \({}^{*}\) Indicates that the result was calculated in this study. **Bold** denotes the best result, while underline indicates the second-best result. For all metrics, higher values are preferable.  
\end{table} 
Table 2: Comparison of quality metrics used for HDR image evaluation. \begin{table}
\begin{tabular}{c c c c} \hline
**Image** & **Resolution** & **HDR-GAN** & **Proposed Model** \\ \hline
001 & 1500 \(\times\) 1000 & 20.54 & 20.83 \\
002 & 1500 \(\times\) 1000 & 20.44 & 20.07 \\
003 & 1500 \(\times\) 1000 & 21.70 & 21.57 \\
004 & 1500 \(\times\) 1000 & 22.64 & 22.92 \\
005 & 1500 \(\times\) 1000 & 16.39 & 16.31 \\
006 & 1500 \(\times\) 1000 & 19.60 & 19.27 \\
007 & 1500 \(\times\) 1000 & 21.35 & 21.51 \\
008 & 1500 \(\times\) 1000 & 18.69 & 18.95 \\
009 & 1500 \(\times\) 1000 & 21.89 & 22.37 \\
010 & 1500 \(\times\) 1000 & 19.56 & 19.47 \\ People Talking & 1500 \(\times\) 1000 & 18.31 & 17.76 \\ Lady Sitting & 1500 \(\times\) 1000 & 21.09 & 21.40 \\ Man Standing & 1500 \(\times\) 1000 & 19.51 & 19.40 \\ People Standing & 1500 \(\times\) 1000 & 16.36 & 16.51 \\ Barbeque Day & 1500 \(\times\) 1000 & 22.91 & 22.65 \\ \hline Average & — & 20.07 & 20.07 \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of IL-NIQE between the HDR-GAN and the proposed model using the Kalantari dataset. The results obtained highlight the proposed model as an efficient and competitive solution for HDR image reconstruction. By replacing conventional convolutions with depthwise separable convolutions, it became possible to achieve a significant reduction in computational cost, evidenced by the reduction in FLOPs, without compromising the visual quality of the generated images. In addition, the model maintained a number of parameters close to those of solutions such as DHDRNet, demonstrating its efficiency. Quantitative analyses, both in traditional and HDR-specific metrics, show that the proposed model is capable of reconstructing HDR images of excellent quality, surpassing or equaling the results of the compared methods. Furthermore, tests performed with dynamic images, using the Kalantari dataset, prove its ability to reconstruct HDR images from exposures with movement.