  For art investigations of paintings, multiple imaging technologies, such as
visual light photography, infrared reflectography, ultraviolet fluorescence
photography, and x-radiography are often used. For a pixel-wise comparison, the
multi-modal images have to be registered. We present a registration and
visualization software tool, that embeds a convolutional neural network to
extract cross-modal features of the crack structures in historical paintings
for automatic registration. The graphical user interface processes the user's
input to configure the registration parameters and to interactively adapt the
image views with the registered pair and image overlays, such as by individual
or synchronized zoom or movements of the views. In the evaluation, we
qualitatively and quantitatively show the effectiveness of our software tool in
terms of registration performance and short inference time on multi-modal
paintings and its transferability by applying our method to historical prints.
