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Image Fine-Grained for Non-uniform Scenes Deblurring

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Artificial Intelligence for Communications and Networks (AICON 2021)

Abstract

Recently, image deblurring has been made advanced progress by various priors and networks. However, it still has room for promoting the image quality of deblurred images, such as details and visual effects of latent images. Therefore, we present an image deblurring method for non-uniform scene deblurring based on image fine-grained strategy. Specifically, we develop building blocks of multi-path fusion blocks (MPFB) and enhancement scale attention modules (ESAM) to recover the fine-grained features of the deblurred image as much as possible. Moreover, we propose multiple loss functions to optimize network training and promote convergence. To demonstrate the effectiveness of the proposed method, subjective and objective comparison experiments are conducted on different datasets. Our method surpasses state-of-the-art (SOTA) methods on synthetic datasets and real images.

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Authors and Affiliations

  1. Qinghai MinZu University, Xining, China

    Qing Qi

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  1. Qing Qi
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Correspondence to Qing Qi .

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Editors and Affiliations

  1. The University of Western Ontario, London, ON, Canada

    Xianbin Wang

  2. University College London, London, UK

    Kai-Kit Wong

  3. Qinghai University for Nationalities, Xining, China

    Shanji Chen

  4. Xidian University, Xi'an, China

    Mingqian Liu

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Qi, Q. (2021). Image Fine-Grained for Non-uniform Scenes Deblurring. In: Wang, X., Wong, KK., Chen, S., Liu, M. (eds) Artificial Intelligence for Communications and Networks. AICON 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 397. Springer, Cham. https://doi.org/10.1007/978-3-030-90199-8_23

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  • DOI: https://doi.org/10.1007/978-3-030-90199-8_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90198-1

  • Online ISBN: 978-3-030-90199-8

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