  Ghost imaging (GI) has been paid attention gradually because of its lens-less
imaging capability, turbulence-free imaging and high detection sensitivity.
However, low image quality and slow imaging speed restrict the application
process of GI. In this paper, we propose a improved GI method based on
Denoising Convolutional Neural Networks (DnCNN). Inspired by the corresponding
between input (noisy image) and output (residual image) in DnCNN, we construct
the mapping between speckles sequence and the corresponding noise distribution
in GI through training. Then, the same speckles sequence is employed to
illuminate unknown targets, and a de-noising target image will be obtained. The
proposed method can be regarded as a general method for GI. Under two sampling
rates, extensive experiments are carried out to compare with traditional GI
method (basic correlation and compressed sensing) and DnCNN method on three
data sets. Moreover, we set up a physical GI experiment system to verify the
proposed method. The results show that the proposed method achieves promising
performance.
