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Efficient feature redundancy reduction for image denoising

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Abstract

It is challenging to deploy convolutional neural networks (CNNs) for image denoising on low-power devices which can suffer from computational and memory constraints. To address this limitation, a simple yet effective and efficient feature redundancy reduction-based network (FRRN) is proposed in this paper, which integrates a feature refinement block (FRB), an attention fusion block (AFB), and an enhancement block (EB). Specifically, the FRB distills structural information via two parallel sub-networks, selecting representative feature representations while suppressing spatial-channel redundancy. The AFB absorbs an attentive fusion mechanism to facilitate diverse features extracted from two sub-networks, emphasizing texture and structure details but alleviating harmful features from problematic regions. The subsequent EB further boosts the feature representation abilities. Aiming to enhance denoising performance at both pixel level and semantic level, a multi-loss scheme comprising three popular loss functions is leveraged to improve the robustness of the denoiser. Comprehensive quantitative and qualitative analyses demonstrate the superiority of the proposed FRRN.

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Data Availability

The training dataset of the gray noisy images is downloaded at https://drive.google.com/file/d/19iNpVSOwmg6QpOixmX6XMSSlVqsHD5sk/view?usp=sharing The training dataset of the color noisy images is downloaded at https://drive.google.com/file/d/1tv5bbg3mnkK4T-Z64jnwiNGDps85URPr/view?usp=sharing The training dataset of real noisy images is downloaded at https://drive.google.com/drive/folders/1NfiXxGevtEe6y4Ve7mELq0_xG4Ewd0O5?usp=sharing Test dataset of Set68 is downloaded at https://drive.google.com/drive/folders/13z2xHWnvdZcld7P5tXoW50sIQJl_4vpZ?usp=sharing Test dataset of Set12 is downloaded at https://drive.google.com/drive/folders/1r3oQJr1pgp4PtNwgsxOfaXA3VL58vQnN?usp=sharing Test dataset of CBSD68 is downloaded at https://drive.google.com/drive/folders/1U1w1O5tUBxBXoxNw2PEFESCzbkTdSgHk?usp=sharing Test dataset of real noisy images is downloaded at https://drive.google.com/drive/folders/15slEqfZvtqKB40FT-netooSXSe8CEP-T?usp=drive_link

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Acknowledgements

This work was supported in part by the High Performance Computing Center of Central South University.

Funding

This work was supported in part by the Natural Science Foundation of China under grant 61836016, in part by the Guangdong Basic and Applied Basic Research Foundation under Grant 2021A1515110079, in part by the Youth Science and Technology Talent Promotion Project of Jiangsu Association for Science and Technology under Grant JSTJ-2023-017, in part by Gusu Innovation and Entrepreneurship Leading Talent under Grant ZXL2023170.

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  1. Yuxuan Hu and Chunwei Tian contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China

    Yuxuan Hu & Sichao Zhang

  2. School of Software, Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China

    Chunwei Tian

  3. Research & Development Institute, Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, China

    Chunwei Tian

  4. Yangtze River Delta Research Institute, Northwestern Polytechnical University, Taicang, 215400, Jiangsu, China

    Chunwei Tian

  5. College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410082, Hunan, China

    Chengyuan Zhang

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Yuxuan Hu, Chunwei Tian, and Shichao Zhang wrote the main manuscript text and Chengyuan Zhang prepared Figures 1-4. All authors reviewed the manuscript.

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Hu, Y., Tian, C., Zhang, C. et al. Efficient feature redundancy reduction for image denoising. World Wide Web 27, 20 (2024). https://doi.org/10.1007/s11280-024-01258-3

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