  This paper proposes a novel framework called concatenated image completion
via tensor augmentation and completion (ICTAC), which recovers missing entries
of color images with high accuracy. Typical images are second- or third-order
tensors (2D/3D) depending if they are grayscale or color, hence tensor
completion algorithms are ideal for their recovery. The proposed framework
performs image completion by concatenating copies of a single image that has
missing entries into a third-order tensor, applying a dimensionality
augmentation technique to the tensor, utilizing a tensor completion algorithm
for recovering its missing entries, and finally extracting the recovered image
from the tensor. The solution relies on two key components that have been
recently proposed to take advantage of the tensor train (TT) rank: A tensor
augmentation tool called ket augmentation (KA) that represents a low-order
tensor by a higher-order tensor, and the algorithm tensor completion by
parallel matrix factorization via tensor train (TMac-TT), which has been
demonstrated to outperform state-of-the-art tensor completion algorithms.
Simulation results for color image recovery show the clear advantage of our
framework against current state-of-the-art tensor completion algorithms.
