Paper
20 October 2023 Image inpainting with gated convolution and interactive multi-scale feature fusion
Kaixing Wang, Jing Chen, Qi Lin, Xueyuan Shen, Xiaoguang Yu
Author Affiliations +
Proceedings Volume 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023); 129160X (2023) https://doi.org/10.1117/12.3004720
Event: Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 2023, Kunming, China
Abstract
Image inpainting aims to fill damaged regions with non-damaged regions and semantic reasonableness while ensuring consistency of an image. The result of inpainting often suffers from smooth edges and blurred details when faced with larger and more complicated damaged regions. In this paper, an end-to-end dual stream network that fuses the texture and structure features, aiming to restore intricate details in filled regions is proposed. For details enhancement, gated convolutions are introduced to pick valid pixels, reducing blur in damaged regions; For more comprehensive features representation, multi-scale parallel dilated convolutions are used to fuse features from different receptive fields and positions in the image. Extensive experimental results on three common datasets demonstrate the superiority of the proposed network in terms of quantitative and qualitative evaluation.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Kaixing Wang, Jing Chen, Qi Lin, Xueyuan Shen, and Xiaoguang Yu "Image inpainting with gated convolution and interactive multi-scale feature fusion", Proc. SPIE 12916, Third International Conference on Signal Image Processing and Communication (ICSIPC 2023), 129160X (20 October 2023); https://doi.org/10.1117/12.3004720
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KEYWORDS
Convolution

Image restoration

Image fusion

Image quality

Feature fusion

Data modeling

Image enhancement

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