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Noise level estimation based on eigenvalue learning

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Abstract

At present, many algorithms use a single minimum eigenvalue to estimate the real noise level, and the levels estimated by these algorithms have been proven to be less than the real noise levels, this is known as underestimation. To address this problem, this paper uses multiple eigenvalues to obtain the relationship between eigenvalues and the real noise level through sample training, calculates the learning coefficients for different noise levels in the relationship expression by linear fitting, and then inputs the learning coefficients into the noise image for noise level estimation. Experiments demonstrate that the algorithm proposed in this paper can significantly improve the underestimation problem of the traditional algorithm and has better estimation accuracy for various noise levels in gray images, color images, and texture images of various scenes.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant nos. 61763009, 61761030, and 62061016), the Doctoral Scientific Fund Project of Hubei Minzu University (Grant no. MD2020 B024), the High-level Scientific Research Achievement Cultivation Project of Hubei Minzu University (Grant no.4205003). We would like to express our gratitude to the anonymous reviewers and editors for their valuable comments and suggestions, which helped to improve the original manuscript.

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  1. School of Mathematics and Statistics, Hubei Minzu University, Enshi, Hubei, 445000, China

    Hanxin Liu, Zhuang Fang & Wenjing Lu

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  1. Hanxin Liu
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  2. Zhuang Fang
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  3. Wenjing Lu
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Correspondence to Zhuang Fang.

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Liu, H., Fang, Z. & Lu, W. Noise level estimation based on eigenvalue learning. Multimed Tools Appl 83, 44503–44525 (2024). https://doi.org/10.1007/s11042-023-17403-5

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