Image Inpainting: A Review

By Omar ElHarrouss, Noor Almaadeed, Somaya Ali Al-Máadeed, Younes Akbari. Neural Processing Letters, 2019

375 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/936d8c76f130aa1a8eb3d994ddfc6e632edaef7a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Phillip E. Pope
2. Xin Jin
3. Tao Yu
4. Samyadeep Basu
5. Wenjun Zeng
6. Jinming Liu
7. Zhibo Chen
8. Soheil Feizi

Citations in the corpus

• Influence Functions in Deep Learning Are Fragile (2020, methodology): “In vision, deep models are frequently used in applications such as object detection [34], image segmentation [35] and inpainting [36].”
• Inpaint Anything: Segment Anything Meets Image Inpainting (2023, background): “Remove Anything focuses on the object removal problem [2, 3, 5] by allowing users to eliminate any object from an image while ensuring that the resulting image remains visually plausible.”