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Frequency Attention Network: Blind Noise Removal for Real Images

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Computer Vision – ACCV 2020 (ACCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12623))

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Abstract

With outstanding feature extraction capabilities, deep convolutional neural networks (CNNs) have achieved extraordinary improvements in image denoising tasks. However, because of the difference of statistical characteristics of signal-dependent noise and signal-independent noise, it is hard to model real noise for training and blind real image denoising is still an important challenge problem. In this work we propose a method for blind image denoising that combines frequency domain analysis and attention mechanism, named frequency attention network (FAN). We adopt wavelet transform to convert images from spatial domain to frequency domain with more sparse features to utilize spectral information and structure information. For the denoising task, the objective of the neural network is to estimate the optimal solution of the wavelet coefficients of the clean image by nonlinear characteristics, which makes FAN possess good interpretability. Meanwhile, spatial and channel mechanisms are employed to enhance feature maps at different scales for capturing contextual information. Extensive experiments on the synthetic noise dataset and two real-world noise benchmarks indicate the superiority of our method over other competing methods at different noise type cases in blind image denoising.

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Author information

Authors and Affiliations

  1. Shanghai Jiao Tong University, Shanghai, 200240, China

    Hongcheng Mo, Jianfei Jiang & Qin Wang

  2. Fullhan Research, Shanghai, China

    Hongcheng Mo, Dong Yin, Pengyu Dong & Jingjun Tian

Authors
  1. Hongcheng Mo
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  2. Jianfei Jiang
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  3. Qin Wang
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  4. Dong Yin
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  5. Pengyu Dong
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  6. Jingjun Tian
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Corresponding author

Correspondence to Qin Wang .

Editor information

Editors and Affiliations

  1. Waseda University, Tokyo, Japan

    Hiroshi Ishikawa

  2. Institute of Automation of Chinese Academy of Sciences, Beijing, China

    Cheng-Lin Liu

  3. Czech Technical University in Prague, Prague, Czech Republic

    Tomas Pajdla

  4. University of Pennsylvania, Philadelphia, PA, USA

    Jianbo Shi

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Mo, H., Jiang, J., Wang, Q., Yin, D., Dong, P., Tian, J. (2021). Frequency Attention Network: Blind Noise Removal for Real Images. In: Ishikawa, H., Liu, CL., Pajdla, T., Shi, J. (eds) Computer Vision – ACCV 2020. ACCV 2020. Lecture Notes in Computer Science(), vol 12623. Springer, Cham. https://doi.org/10.1007/978-3-030-69532-3_11

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  • DOI: https://doi.org/10.1007/978-3-030-69532-3_11

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  • Online ISBN: 978-3-030-69532-3

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