Abstract
With the development of deep learning, there are a lot of inspiring and outstanding attempts in image inpainting. However, the designed models of most existing approaches take up considerable computing resources, which result in sluggish inference speed and low compatibility to small-scale devices. To deal with this issue, we design and propose a lightweight pyramid inpainting Network called LPI-Net, which applies lightweight modules into the inpainting network with pyramidal hierarchy. Besides, the operations in the top-down pathway of the proposed pyramid network are also lightened and redesign for the implementation of lightweight design. According to the qualitative and quantitative comparison of this paper, the proposed LPI-Net outperforms known advanced inpainting approaches with much fewer parameters. In the evaluation inpainting performance on 10–20% damage regions, LPI-Net achieves an improvement of at least 3.52 dB of PSNR than other advanced approaches on CelebA dataset.
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Acknowledgements
This research is supported by Sichuan Provincial Science and Technology Program (No. 2020YFS0307), National Natural Science Foundation of China (No. 61907009), Science and Technology Planning Project of Guangdong Province (No. 2019B010150002). Supported by Postgraduate Innovation Fund Project by Southwest University of Science and Technology (No. 20ycx0002).
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Li, S., Lu, L., Xu, K., Yu, W., Jiang, N., Yang, Z. (2020). LPI-Net: Lightweight Inpainting Network with Pyramidal Hierarchy. In: Yang, H., Pasupa, K., Leung, A.CS., Kwok, J.T., Chan, J.H., King, I. (eds) Neural Information Processing. ICONIP 2020. Communications in Computer and Information Science, vol 1332. Springer, Cham. https://doi.org/10.1007/978-3-030-63820-7_51
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DOI: https://doi.org/10.1007/978-3-030-63820-7_51
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