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MRDA-Net: Multiscale Residual Dense Attention Network for Image Denoising

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Advances in Artificial Intelligence and Security (ICAIS 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1586))

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Abstract

Deep learning methods have become prevalent in image denoising. Existing deep-learning approaches can mainly be divided into two categories: encoder-decoder and high-resolution models, wherein high-resolution models have superior resolution capabilities in detail description and restoration. In this study, we propose a network, namely, multi-scale residual dense attention network, that takes advantage of the context and attention information of images. Specifically, a residual dilated dense module containing dilated dense convolutional layers is employed to enlarge the receptive field of the proposed network. Then, we train such module to learn multi-scale features of images. A feature aggregation module is sequentially designed with dual attention blocks, yielding multi-scale feature maps effectively. We aggregate the multi-scale maps by a concatenation operation. Finally, a simple convolutional layer is adopted to generate residual images. With the residual learning strategy, clean images are obtained implicitly. During the experiment tests, we firstly carry out ablation studies to demonstrate the function of each proposed module. Then, blind and non-blind denoising procedures are carried out. Additionally, the total parameters are considered for analyzing the complexity of the proposed network. Comprehensive experiments show our method is better than several selected state-of-the-art denoising ones by the measure of PSNR and SSIM.

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Author information

Authors and Affiliations

  1. College of Internet of Things Engineering, Hohai University, Changzhou, 213022, China

    Jianhu Zhu, Cheng Yao, Yibin Tang & Yuan Gao

  2. School of Information Science and Engineering, Southeast University, Nanjing, 210096, China

    Lin Zhou

  3. Medizinische Physik, Carl von Ossietzky Universität Oldenburg and Cluster of Excellence “Hearing4all”, Küpkersweg 74, 26129, Oldenburg, Germany

    Hongmei Hu

Authors
  1. Jianhu Zhu
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  2. Cheng Yao
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  3. Yibin Tang
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  4. Yuan Gao
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  5. Lin Zhou
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  6. Hongmei Hu
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Corresponding author

Correspondence to Cheng Yao .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Xiaorui Zhang

  3. Jinan University, Guangzhou, China

    Zhihua Xia

  4. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zhu, J., Yao, C., Tang, Y., Gao, Y., Zhou, L., Hu, H. (2022). MRDA-Net: Multiscale Residual Dense Attention Network for Image Denoising. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Advances in Artificial Intelligence and Security. ICAIS 2022. Communications in Computer and Information Science, vol 1586. Springer, Cham. https://doi.org/10.1007/978-3-031-06767-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-06767-9_18

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  • Online ISBN: 978-3-031-06767-9

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