  We tackle the image reassembly problem with wide space between the fragments,
in such a way that the patterns and colors continuity is mostly unusable. The
spacing emulates the erosion of which the archaeological fragments suffer. We
crop-square the fragments borders to compel our algorithm to learn from the
content of the fragments. We also complicate the image reassembly by removing
fragments and adding pieces from other sources. We use a two-step method to
obtain the reassemblies: 1) a neural network predicts the positions of the
fragments despite the gaps between them; 2) a graph that leads to the best
reassemblies is made from these predictions. In this paper, we notably
investigate the effect of branch-cut in the graph of reassemblies. We also
provide a comparison with the literature, solve complex images reassemblies,
explore at length the dataset, and propose a new metric that suits its
specificities.
  Keywords: image reassembly, jigsaw puzzle, deep learning, graph, branch-cut,
cultural heritage
