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MambaDW: Semantic-Aware Mamba for Document Watermark Removal

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Advances in Computer Graphics (CGI 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15338))

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Abstract

Watermark removal is essential for Optical Character Recognition and digital document reconstruction. Document images differ significantly from natural scene images, necessitating tailored image processing methods. Traditional Transformer-based methods, though effective, face scalability issues due to the quadratic complexity of attention computations. Inspired by Mamba, which scales linearly with context length, we introduce a semantically-guided Mamba approach, MambaDW. This two-stage method first removes most watermarks and then employs semantic-guided 2D selective scanning for precise enhancement and global information fusion. We also incorporate a self-supervised loss to enhance generalizability on unlabeled real data. Our experiments show MambaDW’s effectiveness in document watermark removal.

This work is supported by Shenzhen Science and Technology Innovation Commission (JSGG20220831105002004).

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Authors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

    Yifan Liu, Mingfu Yan, Jiancheng Huang & Shifeng Chen

  2. Southern University of Science and Technology, Shenzhen, China

    Yifan Liu

  3. Northwestern Polytechnical University, Xi’an, China

    He Hua

Authors
  1. Yifan Liu
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  2. Mingfu Yan
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  3. He Hua
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  4. Jiancheng Huang
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  5. Shifeng Chen
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Corresponding author

Correspondence to Shifeng Chen .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. The University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  3. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  4. University of Houston, Houston, TX, USA

    Zhigang Deng

  5. EPFL, Lausanne, Switzerland

    Daniel Thalmann

  6. The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Ping Li

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Liu, Y., Yan, M., Hua, H., Huang, J., Chen, S. (2025). MambaDW: Semantic-Aware Mamba for Document Watermark Removal. In: Magnenat-Thalmann, N., Kim, J., Sheng, B., Deng, Z., Thalmann, D., Li, P. (eds) Advances in Computer Graphics. CGI 2024. Lecture Notes in Computer Science, vol 15338. Springer, Cham. https://doi.org/10.1007/978-3-031-81806-6_12

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  • DOI: https://doi.org/10.1007/978-3-031-81806-6_12

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  • Print ISBN: 978-3-031-81805-9

  • Online ISBN: 978-3-031-81806-6

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