  The advent of deep learning in the past decade has significantly helped
advance image inpainting. Although achieving promising performance, deep
learning-based inpainting algorithms still struggle from the distortion caused
by the fusion of structural and contextual features, which are commonly
obtained from, respectively, deep and shallow layers of a convolutional
encoder. Motivated by this observation, we propose a novel progressive
inpainting network that maintains the structural and contextual integrity of a
processed image. More specifically, inspired by the Gaussian and Laplacian
pyramids, the core of the proposed network is a feature extraction module named
GLE. Stacking GLE modules enables the network to extract image features from
different image frequency components. This ability is important to maintain
structural and contextual integrity, for high frequency components correspond
to structural information while low frequency components correspond to
contextual information. The proposed network utilizes the GLE features to
progressively fill in missing regions in a corrupted image in an iterative
manner. Our benchmarking experiments demonstrate that the proposed method
achieves clear improvement in performance over many state-of-the-art inpainting
algorithms.
