In this work, we used two volume post-processing approaches that both depart substantially from the traditional approach in the field, which is the application of global B-sharpening. One of the approaches is our previously introduced LocalDeblur sharpening method (Ramírez-Aportela, Maluenda et al., 2020 ▸). The second approach is a totally new method based on deep learning (Sanchez-Garcia, Gomez-Blanco et al., 2020 ▸). Concentrating on the latter, this method, DeepEMhancer, relies on a common approach in modern pattern recognition in which a convolutional neural network (CNN) is trained on a known data set comprised of pairs of data points and targets, with the aim of predicting the targets for new unseen data points. In this case, the training was performed by presenting the CNN with pairs of cryo-EM maps collected from the EMDB and maps derived from the structural models associated with the experimental maps. As a result, our CNN learned how to obtain much cleaner and detailed versions of the experimental cryo-EM maps, improving their interpretability.