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Authors: Huilai Liang 1 ; Xichong Ling 2 and Siyu Xia 1

Affiliations: 1 School of Automation, Southeast University, Nanjing, China ; 2 Department of Computing, McGill University, Montreal, Canada

Keyword(s): Image Inpainting, GAN, Deep Fusion.

Abstract: With the recent development of deep learning technique, automatic image inpainting has gained wider applications in computer vision and has also become a challenging topic in image processing. Recent methods typically make use of texture features in the images to make the results more realistic. However this can lead to artifacts in the processed images, one of the reasons for this is that the structural features in the image are ignored. To address this problem, we propose an image inpainting method based on deep fusion of texture and structure. Specifically, we design a dual-pyramid encoder-decoder network for preliminary fusion of texture and structure. A layer-by-layer fusion network of texture and structure is applied to further strengthen the fusion of texture and structure feature afterwards. In order to strengthen the consistency of texture and structure, we construct a multi-gated feature merging network to achieve a more realistic inpainting effect. Experiments are conducte d on the CelebA and Place2 datasets. Qualitative and quantitative comparison demonstrate that our model outperforms state-of-the-art models. (More)

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Paper citation in several formats:
Liang, H., Ling, X. and Xia, S. (2023). Image Inpainting Network Based on Deep Fusion of Texture and Structure. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 147-155. DOI: 10.5220/0011718100003411

@conference{icpram23,
author={Huilai Liang and Xichong Ling and Siyu Xia},
title={Image Inpainting Network Based on Deep Fusion of Texture and Structure},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={147-155},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011718100003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Image Inpainting Network Based on Deep Fusion of Texture and Structure
SN - 978-989-758-626-2
IS - 2184-4313
AU - Liang, H.
AU - Ling, X.
AU - Xia, S.
PY - 2023
SP - 147
EP - 155
DO - 10.5220/0011718100003411
PB - SciTePress

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