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Deep 2nd-order residual block for image denoising

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Abstract

Deep convolutional neural networks (CNNs) play an important role in learning image prior information for image denoising in recent years. However, the current plain networks suffer from feature extraction of weak textures, leading to the loss of image detail. In this paper, inspired by the insights in the connections of the classical deep residual block design and Taylor’s expansion, we propose a deep 2nd-order residual block to enhance the feature extraction ability. The proposed deep 2nd-order residual block combines the dilated convolution, the channel attention mechanism, and the self-ensemble strategy together to improve the denoising performance. Extensive experiments demonstrate that our deep 2nd-order residual block outperforms state-of-the-art image-denoising methods, while also serving as an excellent plug-and-play prior.

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Acknowledgements

This research was sponsored in part by the National Natural Science Foundation of China (Grant No. 62002327, 61976190), Natural Science Foundation of Zhejiang Province (Grant No. Q21F020057), and Key Research and Development Program of Zhejiang Province (Grant No. 2020C03070).

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Authors and Affiliations

  1. College of Information Engineering, Zhejiang University of Technology, Hangzhou, China

    Zan Chen & Yuanjing Feng

  2. School of Computing Sciences, University of East Anglia, Norwich, UK

    Yi Ren

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  1. Zan Chen
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  2. Yuanjing Feng
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  3. Yi Ren
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Correspondence to Zan Chen.

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Chen, Z., Feng, Y. & Ren, Y. Deep 2nd-order residual block for image denoising. Multimed Tools Appl 82, 2101–2119 (2023). https://doi.org/10.1007/s11042-022-13241-z

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  • DOI: https://doi.org/10.1007/s11042-022-13241-z

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