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Frequency Information Matters for Image Matting

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Part of the book series:Lecture Notes in Computer Science ((LNCS,volume 14406))

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Abstract

Image matting aims to estimate the opacity of foreground objects in order to accurately extract them from the background. Existing methods are only concerned with RGB features to obtain alpha mattes, limiting the perception of local tiny details. To address this issue, we introduce frequency information as an auxiliary clue to accurately distinguish foreground boundaries and propose theFrequencyMattingNetwork (FMN). Specifically, we deploy a Frequency Boosting Module (FBM) in addition to the Discrete Cosine Transform (DCT) to extract frequency information from input images. The proposed FBM is a learnable component that empowers the model to adapt to complex scenarios. Furthermore, we design a Domain Aggregation Module (DAM) to effectively fuse frequency features with RGB features. With the assistance of frequency clues, our proposed FMN achieves significant improvements in matting accuracy and visual quality compared with state-of-the-art methods. Extensive experiments on Composition-1k and Distinctions-646 datasets demonstrate the superiority of introducing frequency information for image matting.

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Author information

Authors and Affiliations

  1. Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China

    Rongsheng Luo, Changxin Gao & Nong Sang

  2. Wuhan National Laboratory For Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Rukai Wei

Authors
  1. Rongsheng Luo

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  2. Rukai Wei

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  3. Changxin Gao

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  4. Nong Sang

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Corresponding author

Correspondence toNong Sang.

Editor information

Editors and Affiliations

  1. Kyushu Institute of Technology, Kitakyushu, Fukuoka, Japan

    Huimin Lu

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

    Michael Blumenstein

  3. Yonsei University, Seoul, Korea (Republic of)

    Sung-Bae Cho

  4. Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  5. Osaka University, Osaka, Ibaraki, Japan

    Yasushi Yagi

  6. Kyushu Institute of Technology, Kitakyushu, Japan

    Tohru Kamiya

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Cite this paper

Luo, R., Wei, R., Gao, C., Sang, N. (2023). Frequency Information Matters for Image Matting. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14406. Springer, Cham. https://doi.org/10.1007/978-3-031-47634-1_7

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