  Although image inpainting, or the art of repairing the old and deteriorated
images, has been around for many years, it has gained even more popularity
because of the recent development in image processing techniques. With the
improvement of image processing tools and the flexibility of digital image
editing, automatic image inpainting has found important applications in
computer vision and has also become an important and challenging topic of
research in image processing. This paper is a brief review of the existing
image inpainting approaches we first present a global vision on the existing
methods for image inpainting. We attempt to collect most of the existing
approaches and classify them into three categories, namely, sequential-based,
CNN-based and GAN-based methods. In addition, for each category, a list of
methods for the different types of distortion on the images is presented.
Furthermore, collect a list of the available datasets and discuss these in our
paper. This is a contribution for digital image inpainting researchers trying
to look for the available datasets because there is a lack of datasets
available for image inpainting. As the final step in this overview, we present
the results of real evaluations of the three categories of image inpainting
methods performed on the datasets used, for the different types of image
distortion. In the end, we also present the evaluations metrics and discuss the
performance of these methods in terms of these metrics. This overview can be
used as a reference for image inpainting researchers, and it can also
facilitate the comparison of the methods as well as the datasets used. The main
contribution of this paper is the presentation of the three categories of image
inpainting methods along with a list of available datasets that the researchers
can use to evaluate their proposed methodology against.
