  In this paper, we present a method for speckle pattern design using deep
learning. The speckle patterns possess unique features after experiencing
convolutions in Speckle-Net, our well-designed framework for speckle pattern
generation. We then apply our method to the computational ghost imaging system.
The standard deep learning-assisted ghost imaging methods use the network to
recognize the reconstructed objects or imaging algorithms. In contrast, this
innovative application optimizes the illuminating speckle patterns via
Speckle-Net with specific sampling ratios. Our method, therefore, outperforms
the other techniques for ghost imaging, particularly its ability to retrieve
high-quality images with extremely low sampling ratios. It opens a new route
towards nontrivial speckle generation by referring to a standard loss function
on specified objectives with the modified deep neural network. It also has
great potential for applications in the fields of dynamic speckle illumination
microscopy, structured illumination microscopy, x-ray imaging, photo-acoustic
imaging, and optical lattices.
