  Image inpainting is an effective method to enhance distorted digital images.
Different inpainting methods use the information of neighboring pixels to
predict the value of missing pixels. Recently deep neural networks have been
used to learn structural and semantic details of images for inpainting
purposes. In this paper, we propose a network for image inpainting. This
network, similar to U-Net, extracts various features from images, leading to
better results. We improved the final results by replacing the damaged pixels
with the recovered pixels of the output images. Our experimental results show
that this method produces high-quality results compare to the traditional
methods.
