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A Slimmer and Deeper Approach to Network Structures for Image Denoising and Dehazing

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Book cover Intelligent Data Engineering and Automated Learning – IDEAL 2020 (IDEAL 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12489))

Abstract

Image denoising and dehazing are representatives of low-level vision tasks. The right trade-off between depth and computational complexity of convolutional neural networks (CNNs) is of significant importance to these problems. Wider feature maps and deeper network are beneficial for better performance, but would increase their complexity. In this paper, we explore a new way in network design, to encourage more convolution layers while decrease the width of feature maps. Such slimmer and deeper architectures can enhance the performance while maintain the same level of computational costs. We experimentally evaluate the performances of the proposed approach on denoising and dehazing, and the results demonstrate that it can achieve the state-of-the-art results on both quantitative measures and qualitative performances. Further experiments also indicate that the proposed approach can be adapted for other image restoration tasks such as super-resolution.

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Authors and Affiliations

  1. The University of Manchester, Manchester, M13 9PL, UK

    Boyan Xu & Hujun Yin

Authors
  1. Boyan Xu
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  2. Hujun Yin
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Correspondence to Hujun Yin .

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Editors and Affiliations

  1. University of Minho, Braga, Portugal

    Cesar Analide

  2. University of Minho, Braga, Portugal

    Paulo Novais

  3. Technical University of Madrid, Madrid, Spain

    David Camacho

  4. University of Manchester, Manchester, UK

    Hujun Yin

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Xu, B., Yin, H. (2020). A Slimmer and Deeper Approach to Network Structures for Image Denoising and Dehazing. In: Analide, C., Novais, P., Camacho, D., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2020. IDEAL 2020. Lecture Notes in Computer Science(), vol 12489. Springer, Cham. https://doi.org/10.1007/978-3-030-62362-3_24

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  • DOI: https://doi.org/10.1007/978-3-030-62362-3_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62361-6

  • Online ISBN: 978-3-030-62362-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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