Applied and Computational Engineering

- The Open Access Proceedings Series for Conferences


Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)

Series Vol. 2 , 22 March 2023


Open Access | Article

Remover: Region-Based Inpainting Algorithm

Huifeixin Chen * 1 , Yining Xu 2
1 College of Information and Intelligence, Hunan Agricultural University, Hunan 410128, China
2 School of Control Science and Engineering, Shandong University, Shandong 250002, China

* Author to whom correspondence should be addressed.

Applied and Computational Engineering, Vol. 2, 151-157
Published 22 March 2023. © 2023 The Author(s). Published by EWA Publishing
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Citation Huifeixin Chen, Yining Xu. Remover: Region-Based Inpainting Algorithm. ACE (2023) Vol. 2: 151-157. DOI: 10.54254/2755-2721/2/20220623.

Abstract

The existing method does not seem to be a killer application that combines image segmentation and inpainting to do image processing tasks for ordinary people. Therefore, we propose a region-based inpainting method, namely Remover. Remover is a method that can analyze the content of images, perform automatic image segmentation tasks with or without manual intervention, and inpainting the segmented part to achieve unwanted objects appearing to have been removed from an image without affecting the content of the image. With the help of open-source code and technical support. Remover stands as an Ubuntu desktop application. The code is under development and will be available at https://github.com/WPCJATH/remover soon.

Keywords

Computer Vision, Machine Learning, Image Inpainting, Detectron2, Edge-Connect

References

1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press.

2. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444.

3. Pouyanfar, S., Sadiq, S., Yan, Y., Tian, H., Tao, Y., Reyes, M. P., ... & Iyengar, S. S. (2018). A survey on deep learning: Algorithms, techniques, and applications. ACM Computing Surveys (CSUR), 51(5), 1-36.

4. Shrestha, A., & Mahmood, A. (2019). Review of deep learning algorithms and architec-tures. IEEE Access, 7, 53040-53065.

5. Nazeri, K., Ng, E., Joseph, T., Qureshi, F. Z., & Ebrahimi, M. (2019, January 1). Edge-Connect: Generative image inpainting with adversarial edge learning. ArXiv.Org.

6. Xie, C., Liu, S., Li, C., Cheng, M.-M., Zuo, W., Liu, X., Wen, S., & Ding, E. (2019, September 3). Image inpainting with learnable bidirectional attention maps. ArXiv.Org.

7. Yang, C., Lu, X., Lin, Z., Shechtman, E., Wang, O., & Li, H. (2016, November 30). High-Resolution Image Inpainting using Multi-Scale Neural Patch Synthesis. ArXiv.Org.

8. Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, & Li Fei-Fei. (2009, June). ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition.

9. Honda, H. (2022, January 18). Digging into Detectron 2 — part 1 - Hiroto Honda. Me-dium. https://medium.com/@hirotoschwert/digging-into-detectron-2-47b2e794fabd.

10. Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster R-CNN: Towards real-time ob-ject detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(6), 1137–1149.

Data Availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

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Volume Title
Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
ISBN (Print)
978-1-915371-19-5
ISBN (Online)
978-1-915371-20-1
Published Date
22 March 2023
Series
Applied and Computational Engineering
ISSN (Print)
2755-2721
ISSN (Online)
2755-273X
DOI
10.54254/2755-2721/2/20220623
Copyright
22 March 2023
Open Access
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Copyright © 2023 EWA Publishing. Unless Otherwise Stated