  We present a framework for computational ghost imaging based on deep learning
and customized pink noise speckle patterns. The deep neural network in this
work, which can learn the sensing model and enhance image reconstruction
quality, is trained merely by simulation. To demonstrate the sub-Nyquist level
in our work, the conventional computational ghost imaging results,
reconstructed imaging results using white noise and pink noise via deep
learning are compared under multiple sampling rates at different noise
conditions. We show that the proposed scheme can provide high-quality images
with a sampling rate of 0.8% even when the object is outside the training
dataset, and it is robust to noisy environments. This method is excellent for
various applications, particularly those that require a low sampling rate, fast
reconstruction efficiency, or experience strong noise interference.
