Propose a deep learning-based approach to image restoration, considering the tradeoff between model complexity, training time, and inference speed in this specific application.

Use a convolutional neural network (CNN) architecture with residual connections for image restoration. This approach balances accuracy and computational resources by reducing the number of parameters and improving model parallelization.