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Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing

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Abstract

Moiré often appears when photographing textured objects, which can seriously degrade the quality of the captured photos. Due to the wide distribution of moiré and the dynamic nature of the moiré textures, it is difficult to effectively remove the moiré patterns. In this paper, we propose a multi-spectral dynamic feature encoding (MSDFE) network for image demoiréing. To solve the issue of moiré with distributed frequency spectrum, we design a multi-spectral dynamic feature encoding module to dynamically encode moiré textures. To remedy the issue of moiré textures with dynamic nature, we utilize a multi-scale network structure to process moiré images at different spatial resolutions. Extensive experimental results indicate that our proposed MSDFE outperforms the state-of-the-art in terms of fidelity and perceptual on benchmarks.

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Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Qiang Dai & Li Zhang

  2. School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xi Cheng

Authors
  1. Qiang Dai

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  2. Xi Cheng

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  3. Li Zhang

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Corresponding author

Correspondence toLi Zhang.

Editor information

Editors and Affiliations

  1. University of the West of England, Bristol, UK

    Elias Pimenidis

  2. Lancaster University, Lancaster, UK

    Plamen Angelov

  3. Digital Innovation, Teeside University, Middlesbrough, UK

    Chrisina Jayne

  4. Democritus University of Thrace, Xanthi, Greece

    Antonios Papaleonidas

  5. The University of the West of England, Bristol, UK

    Mehmet Aydin

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Dai, Q., Cheng, X., Zhang, L. (2022). Multi-spectral Dynamic Feature Encoding Network for Image Demoiréing. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_13

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