Deep Learning Denoising vs. Traditional Reconstruction: Advantages & Disadvantages

'Lack of interpretability': Deep learning models are often considered 'black boxes,' making it difficult to interpret the results obtained from them. In contrast, traditional reconstruction methods provide a clear interpretation of the data and the process used to reconstruct it.

'Computationally expensive': Deep learning models require a lot of computational resources, making them impractical for applications that require real-time processing. Traditional reconstruction methods are much faster and can be implemented in real-time.

'Limited data availability': Deep learning models require large amounts of training data to achieve their full potential. However, in some cases, such data may not be available, making it difficult to train the model effectively. Traditional reconstruction methods do not require large amounts of data, making them more accessible in such cases.

'Overfitting': Deep learning models are prone to overfitting the training data, which can lead to poor generalization performance. Traditional reconstruction methods are less prone to overfitting and can provide more robust results.

'Lack of control': Deep learning models may not provide the level of control that is required in some applications. Traditional reconstruction methods allow for more control over the reconstruction process, making them more suitable for certain applications.