Image inpainting for irregular holes based on generative adversarial networks

Ting Liu; Zijun Gao; Wei Li

doi:10.1117/12.3029175

6 May 2024 Image inpainting for irregular holes based on generative adversarial networks

Ting Liu, Zijun Gao, Wei Li

Proceedings Volume 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024); 1310741 (2024) https://doi.org/10.1117/12.3029175
Event: Fourth International Conference on Sensors and Information Technology (ICSI 2024), 2024, Xiamen, China

Abstract

Image inpainting aims to recover the missing regions in an image and reconstruct a satisfactory restoration result with high quality. To solve the problem that the existing image inpainting methods do not deal with the information of missing and non-missing regions flexibly, and the global and local restoration semantics are inconsistent, we design a three-stage restoration model for the different semantic information required for restored regions at different scales, which utilizes different sizes of receptive fields to provide better image details at multiple scales, including global and local, and ensures semantic consistency of contextual information. Experimenting with our method on three popular publicly available image drawing datasets, the results show that this paper's method outperforms current restoration models.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ting Liu, Zijun Gao, and Wei Li "Image inpainting for irregular holes based on generative adversarial networks", Proc. SPIE 13107, Fourth International Conference on Sensors and Information Technology (ICSI 2024), 1310741 (6 May 2024); https://doi.org/10.1117/12.3029175

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KEYWORDS

Image restoration

Image processing

Network architectures

Semantics

Data modeling

Image quality

Convolution

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