To extend the proposed HATNet for color single-pixel imaging or other computational imaging modalities beyond SPI, several modifications and enhancements can be considered:
Color Imaging: For color single-pixel imaging, the HATNet architecture can be adapted to handle multiple color channels. This can involve modifying the input and output layers to accommodate RGB or other color spaces. Additionally, the deep denoiser component can be adjusted to process color information effectively.
Multi-Modal Imaging: To handle other computational imaging modalities, the network can be expanded to incorporate different types of measurements or sensing modalities. This may require changes in the measurement matrices and the reconstruction process to suit the specific characteristics of the new modality.
Transfer Learning: Leveraging transfer learning techniques, the pre-trained HATNet model for SPI can be fine-tuned on new datasets from different imaging modalities. This approach can help adapt the network to new data distributions and imaging characteristics.
Hybrid Architectures: Developing hybrid architectures that combine the strengths of HATNet with domain-specific models or components can enhance the network's capability to handle diverse imaging modalities effectively.
By incorporating these strategies, the HATNet framework can be extended to address color single-pixel imaging and other computational imaging modalities beyond SPI.