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2020 | OriginalPaper | Chapter

LPI-Net: Lightweight Inpainting Network with Pyramidal Hierarchy

Authors : Siyuan Li, Lu Lu, Kepeng Xu, Wenxin Yu, Ning Jiang, Zhuo Yang

Published in: Neural Information Processing

Publisher: Springer International Publishing

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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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Metadata
Title
LPI-Net: Lightweight Inpainting Network with Pyramidal Hierarchy
Authors
Siyuan Li
Lu Lu
Kepeng Xu
Wenxin Yu
Ning Jiang
Zhuo Yang
Copyright Year
2020
Book
Neural Information Processing

Print ISBN: 978-3-030-63819-1

Electronic ISBN: 978-3-030-63820-7

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-3-030-63820-7_51

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