  A single intensity-only holographic interferogram can records the full
amplitude and phase information of optical field. However, current digital
holography technologies cannot recover the lossless phase information from a
single interferogram. In this paper, we provide an entirely new approach for
the full-field quantitative phase imaging technology. We demonstrate that deep
learning can be used to replace the entitative phase shifter, and quantitative
phase imaging can obtain quantitative phase from a single interferogram in
in-line holography. A deep-phase-shift network (DPS-net) is reported, which can
be trained with simulation training data. The trained DPS-net can be used to
generate multiple interferograms with arbitrary phase shift from a single
interferogram as an artificial intelligence phase shifter. The ability and the
accuracy of generating arbitrary phase shifts are verified, and the performance
of the proposed method is also verified by the experimental interferogram. The
results demonstrate that the proposed method can provide a full digital phase
shifter with high-accuracy for the technology of dynamic quantitative phase
measurement.
