Inpaint Image Enchancement Analsysis in Dotnet

Although image inpainting, or the art of repairing the old and deteriorated images, has been around for many years, it has recently 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 reviews the existing image inpainting approaches, that were classified into three subcategories, sequential-based, CNN-based, and GAN-based methods. In addition, for each category, a list of methods for different types of distortion on images are presented. Furthermore, the paper also presents available datasets. Last but not least, we present the results of real evaluations of the three categories of image inpainting methods performed on the used datasets, for different types of image distortion.