  X-radiography (X-ray imaging) is a widely used imaging technique in art
investigation. It can provide information about the condition of a painting as
well as insights into an artist's techniques and working methods, often
revealing hidden information invisible to the naked eye. In this paper, we deal
with the problem of separating mixed X-ray images originating from the
radiography of double-sided paintings. Using the visible color images (RGB
images) from each side of the painting, we propose a new Neural Network
architecture, based upon 'connected' auto-encoders, designed to separate the
mixed X-ray image into two simulated X-ray images corresponding to each side.
In this proposed architecture, the convolutional auto encoders extract features
from the RGB images. These features are then used to (1) reproduce both of the
original RGB images, (2) reconstruct the hypothetical separated X-ray images,
and (3) regenerate the mixed X-ray image. The algorithm operates in a totally
self-supervised fashion without requiring a sample set that contains both the
mixed X-ray images and the separated ones. The methodology was tested on images
from the double-sided wing panels of the \textsl{Ghent Altarpiece}, painted in
1432 by the brothers Hubert and Jan van Eyck. These tests show that the
proposed approach outperforms other state-of-the-art X-ray image separation
methods for art investigation applications.
