While the two-stage approach in CCSR offers significant advantages, it also presents potential limitations. One limitation is the reliance on the quality of the output from the first stage, as any inaccuracies in structure generation can adversely affect the detail enhancement performed by the GAN in the second stage. This dependency may lead to a compounding of errors, particularly in challenging scenarios with complex textures or significant degradation.
To address this limitation, future work could explore the integration of feedback mechanisms between the two stages. For instance, implementing a loop where the GAN can provide feedback to the diffusion model about the quality of the generated structures could help refine the output iteratively. Additionally, enhancing the training process with more robust loss functions that account for both structure and detail fidelity could improve the overall performance of the framework.
Another potential limitation is the computational cost associated with training and inference, especially when fine-tuning the GAN. Future research could focus on optimizing the training process, perhaps by employing techniques such as knowledge distillation or model pruning to reduce the computational burden while maintaining performance.