  The challenge of image generation has been effectively modeled as a problem
of structure priors or transformation. However, existing models have
unsatisfactory performance in understanding the global input image structures
because of particular inherent features (for example, local inductive prior).
Recent studies have shown that self-attention is an efficient modeling
technique for image completion problems. In this paper, we propose a new
architecture that relies on Distance-based Weighted Transformer (DWT) to better
understand the relationships between an image's components. In our model, we
leverage the strengths of both Convolutional Neural Networks (CNNs) and DWT
blocks to enhance the image completion process. Specifically, CNNs are used to
augment the local texture information of coarse priors and DWT blocks are used
to recover certain coarse textures and coherent visual structures. Unlike
current approaches that generally use CNNs to create feature maps, we use the
DWT to encode global dependencies and compute distance-based weighted feature
maps, which substantially minimizes the problem of visual ambiguities.
Meanwhile, to better produce repeated textures, we introduce Residual Fast
Fourier Convolution (Res-FFC) blocks to combine the encoder's skip features
with the coarse features provided by our generator. Furthermore, a simple yet
effective technique is proposed to normalize the non-zero values of
convolutions, and fine-tune the network layers for regularization of the
gradient norms to provide an efficient training stabiliser. Extensive
quantitative and qualitative experiments on three challenging datasets
demonstrate the superiority of our proposed model compared to existing
approaches.
