In recent years, deep learning has garnered significant interest due to its ability to achieve state-of-the-art results across a wide range of applications, including image processing. By leveraging powerful algorithms and vast amounts of data, deep learning has produced remarkable results in tasks such as image classification, object detection and semantic segmentation, through Convolutional Neural Networks architectures. This is due to their capabilities in learning high-level representations of the data, capturing the underlying patterns and extracting features from the images. As a result, traditional image processing algorithms have been outperformed by Artificial Intelligence techniques in terms of accuracy and efficiency, and researchers have been able to tackle more challenging and complex tasks with greater ease.