## 5 Conclusions and Future Work  
This paper presents a lightweight architecture for HDR image reconstruction based on a GAN, using techniques to reduce the computational cost while maintaining the visual quality of the image. The training was conducted with the Kalantari dataset, and the HDR-Eye, Sen, Kalantari, Tursun, and Tel datasets were also used for the experiments. The HDR-GAN architecture was used as a basis for the proposed model, whose optimization was performed through the application of the depthwise separable convolution technique to replace standard convolution. The validation of the proposed model was performed through quantitative and qualitative analyses, comparing the results with the Patch-Based, DHDRNet, HDR-GAN, and Lopez-Cabrejos et al. reconstruction models. For the quantitative evaluation, the metrics PSNR\(-\mu\), PSNR\(-l\), PSNR\(-PU\), SSIM\(-\mu\), SSIM\(-l\), SSIM\(-PU\), HDR-VDP2, HDR-VDP3, and IL-NIQE were used. The computational cost was analyzed based on the number of parameters, FLOPs, model size, inference time, and GPU memory consumption. In the qualitative assessment, different challenges related to the magnitude of movement and saturation ratio were explored. The results of the quantitative analyses demonstrated that the proposed model achieved competitive quantitative metrics, with a PSNR\(-\mu\) value of 43.51 dB when compared to the DHDRNet model, which has a similar computational complexity. While the Lopez-Cabrejos et al. model achieved higher scores in some metrics, with 42.07 in PSNR-l  
Figure 10: Analysis of image 009 of [PERSON] et al. dataset [10], evaluating the ability to reconstruct occluded objects due to movement and the preservation of building shapes. and 67.79 in HDR-VDP2, its evaluation was limited to fewer metrics. In comparison with the HDR-GAN model, the performance results in the reconstruction of the HDR image remained close. When comparing the computational cost of the models, the proposed model had a 6-fold reduction in FLOPs, a 7-fold reduction in the number of parameters, a 7.5-fold reduction in model size, with 1.45 MB versus 10.9 MB, faster inference time, with 0.15 s versus 0.19 s, and lower GPU memory consumption, with 4.5 GB versus 8.3 GB, evidencing the efficiency of the proposed model in the search for a better balance between visual quality and computational cost. In addition, the proposed method achieved remarkable values in the SSIM\(-\mu\) metrics with 0.9917 and SSIM\(-l\) with 0.9887, surpassing the other evaluated models. Using a selection of images from Kalantari and HDR-Eye, the proposed method achieved the highest IL-NIQE value of 24.52, proving its ability in the reconstruction of HDR images from exposures with movement and the ability of the model to generalize to different scenarios. Regarding the qualitative analysis, the proposed method presented adequate behavior, highlighting the reconstruction of areas saturated by overexposure and object alignment. The results obtained and presented in this paper highlight the potential of the proposed model and indicate several promising paths for future research in the development of more efficient and generalizable architectures for HDR image reconstruction. These include the development of a proprietary generative model, due to the visible margin of improvement in the quality of HDR image reconstruction, particularly in metrics where the [PERSON] et al. model showed superior performance, performance of statistical significance analysis, and the use of a training dataset with a greater variety of scenarios in terms of luminosity, motion magnitude, and saturation ratio, which should be addressed in future work. Additional optimization of inference time could also be explored to further improve the model's practical applicability. Conceptualization, G.d.S.F., T.P. and A.B.A. ; methodology, G.d.S.F. ; software, G.d.S.F. ; validation, G.d.S.F., T.P. and A.B.A. ; formal analysis, G.d.S.F. ; investigation, G.d.S.F., T.P. and A.B.A. ; resources, A.B.A. ; data curation, G.d.S.F.