The proposed method can be adapted for real-time applications by optimizing the model architecture and training process to reduce inference time. Techniques such as model quantization, pruning, and parallel processing can be employed to make the restoration process faster without compromising on quality. Additionally, implementing the algorithm on hardware accelerators like GPUs or TPUs can further enhance its speed and efficiency for real-time image restoration tasks.