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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2020

06.12.2019

Image Inpainting: A Review

verfasst von: Omar Elharrouss, Noor Almaadeed, Somaya Al-Maadeed, Younes Akbari

Erschienen in: Neural Processing Letters | Ausgabe 2/2020

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Abstract

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. 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.
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Metadaten
Titel
Image Inpainting: A Review
verfasst von
Omar Elharrouss
Noor Almaadeed
Somaya Al-Maadeed
Younes Akbari
Publikationsdatum
06.12.2019
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2020
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-019-10163-0

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