## 4 Deep Learning Methods for HDR Reconstruction
### GAN for HDR Reconstruction  
This architecture was developed in 2014, but between 2018 and 2021, GANs [25] experienced their most significant surge in interest within the scientific community. In that sense, according to [125], [PERSON] stated that GANs were the \"most interesting idea in the last ten years of machine learning\".  
GANs have proven to be a valuable tool for generating high-quality data across various fields, including image generation and reconstruction [126; 127], video synthesis [128], data augmentation [129], style transfer [130], natural language processing [131], the medical domain [132], remote sensing [133], autonomous vehicles [134], pattern classification with imbalanced data [135], and anomaly detection in time series [24; 46].  
GANs operate by setting up competition between a generator model, which aims to produce samples that mimic the distribution of the original data, and a discriminator model that seeks to determine whether the sample under analysis is real or fake. This competition drives improvements in both models [25]. Figure 8 illustrates how GANs function, highlighting the interaction between generator and discriminator models.  
The main advantage of this architecture lies in the fact that the generator uses a distribution to directly sample from data without needing premodeling, i.e., without making explicit assumptions or approximations about the data distribution before generating new samples, thus theoretically matching the entire distribution of original data [136]. Moreover, compared to other generative architectures, GANs require fewer restrictions to be used with different data types, making them more flexible [125], and their training does not rely  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Model** & **PSNR-L (dB)** & **PSNR-\(\boldsymbol{\mu}\) (dB)** & **SSIM** & **SSIM-\(\boldsymbol{\mu}\)** & **HDR-VDP2** & **LPIPS** \\ \hline \multicolumn{8}{c}{Single-frame} \\ \hline HDRUNet [41] & 41.61 & 34.02 & — & — & — & — \\ ArtHDR-Net [124] & 35 & — & 0.93 & — & 69 & — \\ DCDR-UNet [45] & 52.47 & 54.71 & 0.9985 & — & 68.68 & 0.0026 \\ \hline \multicolumn{8}{c}{Multi-frame} \\ \hline APNT-Fusion [117] & — & 43.96 & — & 0.9957 & — & — \\ IREANet [121] & 39.78 & — & 0.9556 & — & — & 0.102 \\ RepUNet [122] & 44.8081 & — & — & — & — & — \\ \hline \multicolumn{8}{c}{Single-frame and Multi-frame} \\ \hline SAMHDR [123] & 52.49 & 46.98 & 0.9991 & 0.9961 & 69.76 & — \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of metrics for each CNN approach.  
on Markov chains [136]. Hence, GANs yield high-quality results for all data types, often producing images with high sharpness, clarity, and more efficient training compared to other architectures [136].  
In the context of HDR image reconstruction, GAN-based models can generate a distribution of original data on light intensity and local contrast [25; 137]. In this sense, numerous studies have been conducted using GANs to aid in recovering regions where image content is missing due to underexposure and overexposure, as well as to address the limitations of existing methods in handling large object movements in a scene.  
HDR image reconstruction with GANs can be subdivided into two specific approaches: single-frame and multi-frame.