  Ghost imaging incorporating deep learning technology has recently attracted
much attention in the optical imaging field. However, deterministic
illumination and multiple exposure are still essential in most scenarios. Here
we propose a ghost imaging scheme based on a novel conjugate-decoding deep
learning framework (Y-net), which works well under both deterministic and
indeterministic illumination. Benefited from the end-to-end characteristic of
our network, the image of a sample can be achieved directly from a pair of
correlated speckles collected by the detectors, and the sample is illuminated
only once in the experiment. The spatial distribution of the speckles encoding
the sample in the experiment can be completely different from that of the
simulation speckles for training, as long as the statistical characteristics of
the speckles remain unchanged. This approach is particularly important to
high-resolution x-ray ghost imaging applications due to its potential for
improving image quality and reducing radiation damage. And the idea of
conjugate-decoding network may also be applied to other learning-based imaging
