The Hyper-EI algorithm's framework can be extended to address various other HSI inverse problems by leveraging its self-supervised learning approach and equivariant imaging principles. For denoising tasks, the algorithm can be adapted to learn the underlying noise patterns in hyperspectral images and effectively remove noise while preserving important spectral information. By incorporating noise modeling and regularization techniques, Hyper-EI can enhance denoising performance.
In the case of compressive sampling, Hyper-EI can be utilized to reconstruct full hyperspectral images from sparse measurements efficiently. By exploiting the inherent structure and redundancies in hyperspectral data, the algorithm can learn to reconstruct missing spectral bands accurately, enabling high-quality recovery from compressed measurements.
For super-resolution tasks, Hyper-EI can be extended to enhance the spatial resolution of hyperspectral images. By incorporating spatial attention mechanisms and learning spatial correlations, the algorithm can generate high-resolution hyperspectral images from lower-resolution inputs. This extension would involve adapting the network architecture to focus on spatial details while maintaining spectral fidelity.