  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. We find that
this model outperforms all prior density prediction approaches. Because the
input is itself a real-space density, the predictions are equivariant to
molecular symmetry transformations even though the model is not constructed to
be. 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. Our work suggests new routes to learning real-space physical
quantities drawing from the established ideas of image processing.
