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A novel image denoising algorithm combining attention mechanism and residual UNet network

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Abstract

Images are easily polluted by noise in the process of acquisition and transmission, which will affect people's understanding and utilization of knowledge and information in images. Therefore, image denoising, as a classic problem, has received extensive attention from researchers. At present, many image denoising methods based on deep learning have been proposed and achieved good performance. However, most existing methods are insufficient in acquiring and utilizing crucial information in the image when removing noise under complex image denoising tasks such as blind denoising and real-world denoising, resulting in the loss of fine details in the reconstructed image. To overcome this shortcoming, in this paper, we propose a novel image denoising algorithm combining attention mechanism and residual UNet network, named Att-ResUNet. Specifically, we propose a novel UNet-based image denoising framework, which employs residual enhancement blocks and skip connections to form global–local residuals, which can fuse multi-scale global context and local features to more thoroughly capture and remove hidden noise in the image. A channel attention mechanism is introduced, which can better focus on the crucial information in the image and improve the denoising performance. In addition, we use adaptive average pooling for down-sampling, which can preserve more image structure information, reduce the loss of edge details, and adopt a residual learning strategy to enhance the learning and expressive capabilities of the denoising model. Extensive experiments on several publicly available standard datasets demonstrate the superiority of our method over 15 state-of-the-art methods and achieve excellent denoising performance. Compared with mainstream methods, our method outperforms current state-of-the-art methods by up to 0.76 dB and 1.10 dB on PSNR evaluation metrics on BSD68 and Set12 datasets, respectively. Notably, our method achieves an average PSNR value of 37.88 dB on the CC dataset in real-world denoising experiments, a significant improvement of 2.14 dB over the most advanced methods.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant Nos. 62276265, 61976216, and 61672522.

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Authors and Affiliations

  1. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, 221116, China

    Shifei Ding, Qidong Wang, Lili Guo & Jian Zhang

  2. Mine Digitization Engineering Research Center of Ministry of Education of the People’s Republic of China, Xuzhou, 221116, China

    Shifei Ding & Lili Guo

  3. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Ling Ding

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  1. Shifei Ding
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Contributions

Prof. Shifei Ding helped in supervision. Dr. Qidong Wang helped in conceptualization and methodology. Dr. Lili Guo helped in supervision. Dr. Jian Zhang worked in software and writing. Dr. Ling Ding helped in supervision. All authors reviewed the manuscript.

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Correspondence to Lili Guo or Ling Ding.

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Ding, S., Wang, Q., Guo, L. et al. A novel image denoising algorithm combining attention mechanism and residual UNet network. Knowl Inf Syst 66, 581–611 (2024). https://doi.org/10.1007/s10115-023-01965-9

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