## 5 Discussions and Future Perspectives
### State of the Art  
The state of the art in HDR reconstruction has progressed considerably, featuring methods ranging from classic approaches based on autoencoders and optical flow to more complex techniques combining attention mechanisms and GANs. Below is a review of the CNN-based models:  
* CNN-Based Models: CNN-based approaches have long dominated the field and remain highly relevant. Works like HDRCNN [8] and variants such as HDRUnet,  
Figure 12: HDR-V-Diff architecture. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Category** & **Model** & **Strengths** & **Limitations** \\ \hline \multirow{3}{*}{Optical Flow-Based} & HFT-HDR [31] & \(\bullet\) & Low inference time & \(\bullet\) & Susceptible to distortions due to \\  & [PERSON] et al. [157], & \(\bullet\) & Highly effective in [PERSON] or less & & inaccurate tracking under large \\  & HDRFlow [32] & \(\bullet\) & dynamic scenes & & movements \\  & & \(\bullet\) & Patch-level alignment & \(\bullet\) & High computational cost \\ \hline \multirow{3}{*}{Attention-Based} & LAN-HDR [33], & \(\bullet\) & Spatial and temporal attention & & \\  & LAN-HDR [33], & \(\bullet\) & Considers global image context & \(\bullet\) & Higher resource usage \\  & HDR-V-Diff [34] & \(\bullet\) & Improved reconstruction in & \(\bullet\) & High memory consumption \\ \cline{1-1}  & & & missing-content areas & & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: HDR video reconstruction models, with their strengths and limitations. HDRTV, and DCDR-UNet highlight the use of convolutional architectures, often complemented by skip connections and deformable modules, to recover lost details in overexposed and underexposed areas [41; 45]. The HDRCNN architecture uses a loss function that separates illuminance and reflectance components, allowing for the adjustment of the relative importance of illuminance through a weighting parameter. Subsequent models, regardless of the employed architecture, frequently adopt variations of the L1 loss function, or even L1 itself, due to its proven effectiveness and consistent performance in image reconstruction tasks. The models we have examined are reviewed below:  
* GAN-Based Models: The application of GANs, as seen in HDR-cGAN [138] and GlowGAN [10], shows potential for producing photorealistic images thanks to the adversarial generator's ability to simulate nuances of exposure and contrast. Typically, GAN models for HDR reconstruction analyzed in this review use a combination of loss functions. The classic adversarial loss (based on the minimax formulation or variations like LSGAN or WGAN) encourages the generator to produce visually plausible results that deceive the discriminator. However, to ensure fidelity to the input image content (LDR) and the reference (HDR ground truth, when available), this adversarial loss is often combined with pixelwise reconstruction losses (like L1, which is preferred over L2 to avoid excessive smoothing) and/or perceptual losses (like LPIPS), which measure similarity in the feature spaces of pretrained networks, better capturing the structural and textural similarity perceived by the human eye [26; 138]. The relative weighting between these losses is a crucial hyperparameter for balancing realism and fidelity. * Transformer-Based Models: Initially prominent in NLP, Transformers have been adapted for HDR image reconstruction in computer vision. Models like CA-ViT [17] and HDT-HDR [161] employ self-attention mechanisms to enhance alignment and fuse multiple exposures, achieving more accurate and detailed reconstructions. Typically, the loss functions used in Transformer-based models for HDR image reconstruction combine the classic loss function in the logarithmic domain with perceptual loss, resulting in the form \(L_{1}+\alpha L_{p}\)[17; 157; 159; 162; 27], aiming to optimize both objective fidelity and the perceived visual quality of the reconstruction.