The conditional generative approach employed in CGDenoiser, while effective, has several potential limitations. One significant concern is the reliance on the quality and diversity of the training dataset. If the dataset does not adequately represent the range of real-world noise and degradation scenarios, the model may struggle to generalize effectively, leading to suboptimal performance in practical applications. Additionally, the complexity of the model may result in overfitting, where the model learns to denoise specific patterns in the training data but fails to perform well on unseen data.
Future research could explore alternative generative modeling techniques, such as diffusion models or flow-based generative models, which may offer improved flexibility and robustness. Diffusion models, for instance, have shown promise in generating high-quality images by gradually denoising a sample from a noise distribution, potentially allowing for better handling of diverse degradations. Furthermore, incorporating unsupervised or semi-supervised learning techniques could help mitigate the dependency on labeled data, enabling the model to learn from a broader range of unlabeled examples. This could enhance the model's ability to adapt to various noise types and improve its overall performance in real-world UAV tracking scenarios.