  A well-trained deep neural network is shown to gain capability of
simultaneously restoring two kinds of images, which are completely destroyed by
two distinct scattering medias respectively. The network, based on the U-net
architecture, can be trained by blended dataset of speckles-reference images
pairs. We experimentally demonstrate the power of the network in reconstructing
images which are strongly diffused by glass diffuser or multi-mode fiber. The
learning model further shows good generalization ability to reconstruct images
that are distinguished from the training dataset. Our work facilitates the
study of optical transmission and expands machine learning's application in
optics.
