## 5 Discussions and Future Perspectives
### Trends and Future Ideas  
Future directions in HDR reconstruction promise exciting avenues:  
* Multi-modal Models: Integrating information from different sensor modalities, such as depth (from LiDAR/Stereo), polarization, or event cameras, can result in more robust and accurate HDR reconstructions. Models leveraging multiple input modalities could capture extra scene nuances, boosting HDR image quality, especially in complex dynamic scenarios.
* Self-Supervised and Unsupervised Learning: Techniques that do not require massive annotated datasets are attracting greater interest. Self-supervised methods can enable HDR reconstruction from unlabeled data, facilitating training in data-scarce settings [50]. Unsupervised approaches, including those using cycle consistency or domain adaptation, are particularly valuable for unpaired LDR-HDR datasets [179].
* Computational Optimization and Energy Efficiency: Developing lighter and more efficient models that maintain high HDR reconstruction quality while reducing resource usage is a growing research area. Pruning, quantization, and knowledge distillation will play a key role in creating models suitable for mobile and real-time applications [122]. The exploration of new and efficient architectures, such as SSMs, notably Mamboa, which offer linear scaling and effective modeling of long-range dependencies, could provide significant efficiency gains for high-resolution images and video compared to traditional Transformers.
* Large Language/Vision Models (LLMs/LVMs): Integrating the perception and semantic understanding capabilities of LLMs and LVMs presents an innovative direction. These models can provide high-level guidance for reconstruction (e.g., identifying objects/regions, suggesting enhancement styles), enable zero-shot or few-shot adaptation to new scenarios, or serve as powerful priors or feature extractors within HDR pipelines, especially for human-knowledge-guided unpaired data reconstruction [179].
* Standardization and Evaluation Protocols: Establishing consistent standards for HDR data collection, labeling, and evaluation--both in terms of metrics (including their correlation with human perception and real-time suitability) and benchmark datasets covering diverse real-world scenarios--would facilitate fair comparisons and promote collaboration within the scientific community [94].
* Advanced Transformers and Attention Mechanisms: The ongoing adaptation and refinement of Transformers in vision tasks are poised to further enhance HDR reconstruction. More sophisticated attention mechanisms, considering relationships between multiple exposures and overall scene structure, may lead to more detailed reconstructions with fewer artifacts [17].  
* Enhanced Diffusion Models: DMs are growing as a viable alternative for generating high-quality HDR images. Future studies will likely focus on reducing their computational complexity, making them more accessible for real-time use and deployment on resource-constrained devices [28; 34].
* Adaptive and Controllable Reconstruction: Future models may offer more user control, allowing for adjustments to tone mapping style, artifact tolerance, or region-specific enhancement interactively, which could be possibly guided by semantic scene understanding.
* Vision Mamba: This is an approach that maintains the high performance characteristic of Transformers but with lower computational cost. Originally presented as Mamba, the technique was extended to computer vision tasks, resulting in the variant called Vision Mamba. This approach has demonstrated superior performance compared to Transformers in various tasks, such as detection, segmentation [180], and image restoration [181]. Future work may investigate the impact of Vision Mamba on the task of HDR image reconstruction, especially in the feature enhancement stage, positioning it as a promising candidate to replace Transformer-based architectures, which are more complex and costly.