  X-ray "ghost" imaging has drawn great attention for its potential to lower
radiation dose in medical diagnosis. For practical implementation, however, the
efficiency and image quality have to be greatly improved. Here we demonstrate a
computational ghost imaging scheme where a bucket detector and specially
designed modulation masks are used, together with a new robust deep learning
algorithm in which a compressed set of Hadamard matrices is incorporated into a
multi-level wavelet convolutional neural network. By this means we have
obtained an image of a real object from only 18.75% of the Nyquist sampling
rate, using a portable tabletop incoherent x-ray source of ~37 {\mu}m diameter.
A high imaging resolution of ~10 {\mu}m is achieved, which represents a
concrete step towards the realization of a practical low cost x-ray ghost
imaging camera for applications in biomedicine, archeology, material science,
and so forth.
