Open Access
| Issue |
ITM Web Conf.
Volume 78, 2025
International Conference on Computer Science and Electronic Information Technology (CSEIT 2025)
|
|
|---|---|---|
| Article Number | 04017 | |
| Number of page(s) | 8 | |
| Section | Foundations and Frontiers in Multimodal AI, Large Models, and Generative Technologies | |
| DOI | https://doi.org/10.1051/itmconf/20257804017 | |
| Published online | 08 September 2025 | |
- L. Zhao, L. Shen, and R. Hong, “Survey on image inpainting research,” Computer Science, vol. 48, no. 3, pp. 14–26, 2021. [Google Scholar]
- S. Iizuka, E. Simo-Serra, and H. Ishikawa, “Globally and locally consistent image completion,” ACM Transactions on Graphics (TOG), vol. 36, no. 4, pp. 1–14, 2017, doi: 10.1145/3072959.3073659. [CrossRef] [Google Scholar]
- X. Shi, K. Zhu, X. Zhang, S. Zhang, and L. Chen, “Occluded face inpainting method based on generative adversarial networks,” Frontiers of Data & Computing, vol. 4, no. 4, pp. 123–131, 2022. [Google Scholar]
- K. Xu, Q. Zhang, J. He, et al., “Facial image inpainting method based on attention mechanism and multi-scale feature aggregation module,” Journal of Hainan Normal University (Natural Science), vol. 37, no. 3, pp. 338–347, 2024. [Google Scholar]
- J. Sun, J. Wu, Z. Shen, and E. Peng, “Face image inpainting algorithm based on mask prediction and multi-scale context aggregation,” Radio Engineering, no. 10, pp. 2251–2260, 2023. [Google Scholar]
- M. Zhang, “Joint GAN face inpainting algorithm based on Swin Transformer and UNet,” Modern Computer, vol. 2024, no. 6, pp. 32–37, 2024. [Google Scholar]
- C. Han and J. L. Wang, “Face image inpainting with evolutionary generators,” IEEE Signal Processing Letters, vol. 28, pp. 190–193, 2021. [Google Scholar]
- L. Yu, D. Q. Zhu, and J. He, “Semantic segmentation guided face inpainting based on SN-PatchGAN,” in Proc. 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), 2020, pp. 110–115. [Google Scholar]
- J. Yu et al., “Free-form image inpainting with gated convolution,” in Proc. ICCV, 2019, pp. 4471–4480. [Google Scholar]
- K. Nazeri et al., “EdgeConnect: Generative image inpainting with adversarial edge learning,” in Proc. ICCV, 2019, pp. 3265–3274. [Google Scholar]
- R. Suvorov et al., “LaMa: Resolution-robust large mask inpainting with Fourier convolutions,” in Proc. CVPR, 2022, pp. 2149–2159. [Google Scholar]
- L. Zhao et al., “CoModGAN: Collaborative GANs for complex image inpainting,” in Proc. NeurIPS, 2021, pp. 24868–24880. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.