  Image inpainting aims at restoring missing regions of corrupted images, which
has many applications such as image restoration and object removal. However,
current GAN-based generative inpainting models do not explicitly exploit the
structural or textural consistency between restored contents and their
surrounding contexts.To address this limitation, we propose to enforce the
alignment (or closeness) between the local data submanifolds (or subspaces)
around restored images and those around the original (uncorrupted) images
during the learning process of GAN-based inpainting models. We exploit Local
Intrinsic Dimensionality (LID) to measure, in deep feature space, the alignment
between data submanifolds learned by a GAN model and those of the original
data, from a perspective of both images (denoted as iLID) and local patches
(denoted as pLID) of images. We then apply iLID and pLID as regularizations for
GAN-based inpainting models to encourage two levels of submanifold alignment:
1) an image-level alignment for improving structural consistency, and 2) a
patch-level alignment for improving textural details. Experimental results on
four benchmark datasets show that our proposed model can generate more accurate
results than state-of-the-art models.
