To further improve the performance of deep unfolding networks for single-pixel imaging reconstruction, exploring different types of attention mechanisms and transformer architectures can be beneficial:
Cross-Modal Attention: Introducing cross-modal attention mechanisms that can effectively capture correlations between different modalities or imaging domains can enhance the network's ability to extract meaningful features and improve reconstruction quality.
Sparse Attention: Incorporating sparse attention mechanisms can help the network focus on relevant image regions or features, leading to more efficient and accurate reconstructions, especially in scenarios with sparse or localized information.
Temporal Attention: For dynamic imaging applications, integrating temporal attention mechanisms can enable the network to leverage temporal dependencies in sequential data, enhancing the reconstruction of time-varying scenes or videos.
Hierarchical Transformers: Implementing hierarchical transformer architectures that combine multiple levels of abstraction and context modeling can improve the network's understanding of complex image structures and relationships, leading to more precise and detailed image reconstruction.
By exploring these advanced attention mechanisms and transformer architectures, deep unfolding networks can achieve higher reconstruction accuracy, robustness, and adaptability for single-pixel imaging and other computational imaging tasks.