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Non-local self-similarity recurrent neural network: dataset and study

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Abstract

The images and videos of the high-voltage copper contact are disturbed by various noises in the factory. In this paper, an improved Non-local Self-similarity Recurrent Neural Network(NSRNN) is proposed for image denoising. The sparse representation is used for initializing the images, and then NSRNN is trained and tested based on the image datasets with different noise levels and magnification. Due to the similarity and the time correlation between the sequential images, RNN is used to improve the parameter utilization and model robustness. By measuring the self-similarity of the neighborhood features, NSRNN model outperforms other state-of-the-art methods in term of image denoising performance.

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Data availability

The processed code or data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Funding

This work was supported by the Shaanxi Provincial Education Department (Program No.21JK0956),and the Natural Science Basic Research Program of Shaanxi(2021JQ-880).

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Authors and Affiliations

  1. School of Science, Xijing University, No.1 Xijing Road, Xi’an, 710123, People’s Republic of China

    Lili Han, Yang Wang, Mingshu Chen, Jiaofei Huo & Hongtao Dang

Authors
  1. Lili Han
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  2. Yang Wang
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  3. Mingshu Chen
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  4. Jiaofei Huo
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  5. Hongtao Dang
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Contributions

Lili Han contributed to the conception of the study. Yang Wang performed the experiment. Mingshu Chen contributed significantly to analysis and manuscript preparation. Jiaofei Huo performed the data analyses and wrote the manuscript. Hongtao Dang helped perform the analysis with constructive discussions.

Corresponding author

Correspondence to Lili Han.

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Han, L., Wang, Y., Chen, M. et al. Non-local self-similarity recurrent neural network: dataset and study. Appl Intell 53, 3963–3973 (2023). https://doi.org/10.1007/s10489-022-03616-y

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