## 4 Experiments
### Qualitative Results  
To verify the performance of the proposed model, a qualitative analysis of scenes from the datasets of [PERSON] et al. [26], [PERSON] et al. [27], [PERSON] et al. [28], and [PERSON] et al. [10] was carried out, where one scene from each dataset was selected to perform the analyses with the Patch-Based [26], DHDRNet [10], HDR-GAN [16], Lopez-Cabrejos [49] models and the proposed model, respectively. The structure of the scene presentation is composed of three different input exposures (LDRs), the image reconstructed by the proposed model, cutouts of the input regions that will be analyzed, and the comparison between the models. Therefore, Figure 7 shows an analysis of the HighChair scene from the [PERSON] et al. dataset, chosen due to its characteristics of slight movements, as indicated in the blue rectangle, and saturated background in the red rectangle, as can be seen in Figure 7a. All models obtained excellent results in the alignment performed in the Cropped Section Figure 7b, demonstrating that the magnitude of movement in this scene was not challenging. Meanwhile, in the Cropped Section Figure 7c, all models reconstructed a smooth background, with the exception of DHDRNet [10] and Lopez-Cabrejos [49], which exhibited artifacts at the image saturation level or oversaturation. \begin{table}
\begin{tabular}{c c c c} \hline
**Image** & **Resolution** & **HDR-GAN** & **Proposed Model** \\ \hline C06 & 5472 \(\times\) 3648 & 24.88 & 25.06 \\ C07 & 5472 \(\times\) 3648 & 25.25 & 25.93 \\ C08 * & 3632 \(\times\) 2288 & 18.74 & 18.28 \\ C10 & 5472 \(\times\) 364  
Figure 8 illustrates the analysis of the scene Toytrain of the [PERSON] et al. [27] dataset, representing low-light conditions and large movements, as shown in detail in Figure 8a. In the Cropped Section Figure 8b shown in the blue rectangle, the Patch-Based [26] model, Lopez-Cabrejos [49] model, and the proposed model presented artifacts in dark scenes, possibly due to the [10] dataset used for training, which contains mostly bright scenes [28]. This suggests that dataset diversification, including different lighting conditions, is essential for models with a low number of parameters. However, the proposed model demonstrated good performance in the reconstruction of large movements in the Cropped Section Figure 8c, highlighted by the red rectangle, while DHDRNet [10] generated light artifacts and the Lopez-Cabrejos model [49] produced a reconstruction of this region with excessive brightness, leading to loss of information. Figure 7: Analysis of the HighChair image of the [PERSON] et al. dataset [26], checking for short motion and a saturated background. Using scene 21-1 from the [PERSON] et al. [28] dataset, presented in Figure 9, containing features of high background saturation and large movement, challenging situations are demonstrated, as can be seen in Figure 9a. In the analyzed area of the red rectangle, in the Cropped Section Figure 9b, the proposed model demonstrated a smooth background and avoided artifacts caused by movement. Unlike this, the HDR-GAN [16] model presented ghosting in this area. Figure 8: Analysis ofimage Toytrain of [PERSON] et al. dataset [27], checking dark scene and reconstruction of objects with large motion. Figure 10 shows scene 009 of the [PERSON] et al. [10] dataset, presenting challenges related to areas occluded by the movement of objects, as indicated in the region under analysis in the blue rectangle and saturated objects in the area of the red rectangle, Figure 10a. All models had difficulties in reconstructing the Cropped Section Figure 10b, while in the Cropped Section Figure 10c, Lopez-Cabrejos [49] demonstrated the best performance in representing the shape of the building, while the proposed model managed to represent it considerably, only presenting divergence in tone.