One potential limitation of the equivariant imaging approach is the computational complexity associated with handling multiple group operators and enforcing invariance constraints. To address this, optimization strategies such as efficient parameter initialization, regularization techniques, and model pruning can be employed to streamline the training process and improve computational efficiency.
Another limitation could be the sensitivity of equivariant imaging to variations in input data distributions or transformations. To enhance robustness, data augmentation techniques can be utilized to expose the model to diverse input variations during training, enabling it to generalize better to unseen data and transformations.
Additionally, the equivariant imaging approach may face challenges in capturing complex spatial and spectral dependencies in hyperspectral data. To overcome this limitation, more sophisticated network architectures, such as graph neural networks or attention mechanisms, can be integrated into Hyper-EI to better model long-range dependencies and intricate spectral correlations, thereby improving overall performance.