## 4 Deep Learning Methods for HDR Reconstruction
### Transformers for HDR Reconstruction  
Models based on attention mechanisms have radically transformed how neural networks process and prioritize information in deep learning tasks. This revolutionary  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Method** & **PSNR-l** & **PSNR-\(\mathbf{\mu}\)** & **PSNR-PU** & **SSIM-l** & **SSIM-\(\mathbf{\mu}\)** & **SSIM-PU** & **MSSIM** & **HDR-VDP2** & **HDR-VDP3** \\ \hline \hline \multicolumn{10}{c}{Single-Frame} \\ \hline GlowGAN [10] & — & — & 31.8 & — & — & — & — & — & 7.44 \\ HDR-cGAN [138] & 17.57 & — & — & 0.78 & — & — & — & 51.94 & — \\ \hline \multicolumn{10}{c}{Multi-Frame} \\ \hline HDR-GAN [26] & 41.76 & 43.64 & 43.2004 & 0.9869 & 0.9891 & 0.9913 & — & 65.45 & — \\ UPHDR-GAN [143] & 43.005 & — & 42.115 & 0.988 & — & 0.986 & — & 63.542 & — \\ MEF-GAN [144] & 68.42 & — & — & — & — & — & 0.982 & — & — \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of metrics for GAN approaches.  
approach was introduced with the proposal of a neural network exclusively based on self-attention mechanisms, known as Transformers [18], marking a milestone in natural language processing (NLP). Such architectures stand out for their ability to focus on different regions of the input, allowing the model to learn and capture complex relationships more effectively and applying a more reliable attention mechanism, but at a higher computational cost, given that self-attention must be iteratively calculated for each input token, implying quadratic complexity.  
The Transformer architecture is mainly composed of two fundamental blocks: the encoder and the decoder. The encoder takes the input sequence and transforms it into an encoded representation, while the decoder uses that representation to generate the output sequence. This architecture has proved highly accurate in managing complex linguistic structures, leading to its rapid adoption and expansion into various domains. The initial success of transformers in NLP led researchers to explore their potential in other fields. In particular, they have been adapted to computer vision, showing promising results in a range of tasks. A notable and pioneering example is the Vision Transformer (ViT) [146], which adapts the Transformer encoder architecture for image classification. In the ViT, an image is split into patches treated as tokens, akin to words in a sentence. This approach enables the ViT to grasp intricate patterns and relationships within the image, achieving superior performance in various benchmarks.  
Today, Transformer-based models have become key tools in many computer vision applications. Their effectiveness has been demonstrated in tasks such as low-light image enhancement [147; 148], content generation [149; 150], super-resolution [49; 151], and more [152; 153; 154; 155]. This versatility underscores the power of Transformers in addressing a broad range of computational challenges.  
In the specific context of HDR image reconstruction, Transformers have shown significant potential for improving the quality and details of images. The most recent models have leveraged the attention mechanisms inherent to transformers to efficiently handle the various levels of luminance and complex scenes typically found in HDR content. Such progress not only boosts accuracy in HDR reconstruction but also fosters greater generalization across different scene types.  
Figure 9 presents the most common approaches to HDR reconstruction, which can be broadly classified into three major methods. The first relies on aligning images with different exposures before the Transformer mechanism, the second focuses on spatial or temporal attention mechanisms for feature extraction, and the third employs methods based on feature concatenation, relying on Transformer mechanisms for the final reconstruction.  
Figure 9: _Cont._