PairingNet: A Learning-based Pair-searching and -matching Network for
  Image Fragments
Rixin Zhou Ding Xia Yi Zhang Honglin Pang Xi Yang Chuntao Li
In this paper, we propose a learning-based image fragment pair-searching and
-matching approach to solve the challenging restoration problem. Existing works
use rule-based methods to match similar contour shapes or textures, which are
always difficult to tune hyperparameters for extensive data and computationally
time-consuming. Therefore, we propose a neural network that can effectively
utilize neighbor textures with contour shape information to fundamentally
improve performance. First, we employ a graph-based network to extract the
local contour and texture features of fragments. Then, for the pair-searching
task, we adopt a linear transformer-based module to integrate these local
features and use contrastive loss to encode the global features of each
fragment. For the pair-matching task, we design a weighted fusion module to
dynamically fuse extracted local contour and texture features, and formulate a
similarity matrix for each pair of fragments to calculate the matching score
and infer the adjacent segment of contours. To faithfully evaluate our proposed
network, we created a new image fragment dataset through an algorithm we
designed that tears complete images into irregular fragments. The experimental
results show that our proposed network achieves excellent pair-searching
accuracy, reduces matching errors, and significantly reduces computational
time. Details, sourcecode, and data are available in our supplementary
material.