  Over the last few years, the performance of inpainting to fill missing
regions has shown significant improvements by using deep neural networks. Most
of inpainting work create a visually plausible structure and texture, however,
due to them often generating a blurry result, final outcomes appear unrealistic
and make feel heterogeneity. In order to solve this problem, the existing
methods have used a patch based solution with deep neural network, however,
these methods also cannot transfer the texture properly. Motivated by these
observation, we propose a patch based method. Texture Transform Attention
network(TTA-Net) that better produces the missing region inpainting with fine
details. The task is a single refinement network and takes the form of U-Net
architecture that transfers fine texture features of encoder to coarse semantic
features of decoder through skip-connection. Texture Transform Attention is
used to create a new reassembled texture map using fine textures and coarse
semantics that can efficiently transfer texture information as a result. To
stabilize training process, we use a VGG feature layer of ground truth and
patch discriminator. We evaluate our model end-to-end with the publicly
available datasets CelebA-HQ and Places2 and demonstrate that images of higher
quality can be obtained to the existing state-of-the-art approaches.
