  Reconstruction of image by using convolutional neural networks (CNNs) has
been vigorously studied in the last decade. Until now, there have being
developed several techniques for imaging of a single object through scattering
medium by using neural networks, however how to reconstruct images of more than
one object simultaneously seems hard to realize. In this paper, we demonstrate
an approach by using generative adversarial network (GAN) to reconstruct images
of two adjacent objects through scattering media. We construct an imaging
system for imaging of two adjacent objects behind the scattering media. In
general, as the light field of two adjacent object images pass through the
scattering slab, a speckle pattern is obtained. The designed adversarial
network, which is called as YGAN, is employed to reconstruct the images
simultaneously. It is shown that based on the trained YGAN, we can reconstruct
images of two adjacent objects from one speckle pattern with high fidelity. In
addition, we study the influence of the object image types, and the distance
between the two adjacent objects on the fidelity of the reconstructed images.
Moreover even if another scattering medium is inserted between the two objects,
we can also reconstruct the images of two objects from a speckle with high
quality. The technique presented in this work can be used for applications in
areas of medical image analysis, such as medical image classification,
segmentation, and studies of multi-object scattering imaging etc.
