<p>Predicting ground-state electron densities of chemical systems has recently received growing attention in machine learning quantum chemistry, given their fundamental importance as highlighted by the Hohenberg-Kohn theorem. Drawing inspiration from the domain of image super-resolution, we view the electron density as a 3D grayscale image and use a convolutional residual network to transform a crude and trivially generated guess of the molecular density into an accurate ground-state quantum mechanical density. Here we show that this model produces more accurate predictions than all prior density prediction approaches. Due to its simplicity, the model is directly applicable to unseen molecular conformations and chemical elements. We show that fine-tuning on limited new data provides high accuracy even in challenging cases of exotic elements and charge states.</p>