In classical machine learning algorithms, for example those used for classification tasks, a common issue arises when dealing with an unbalanced training dataset. When certain classes are under-represented, it can result in poor performance not only on those classes but also on average. This issue also occurs in Image Enhancement algorithms, where some types of images may be under-represented in the training dataset. Consequently, the performance on these types of images tends to be lower. The most common solution to deal with an unbalance dataset in classical machine learn- ing is to resample the dataset, in order to have a balanced representation of all classes.