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Image denoising method based on improved wavelet threshold algorithm

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Abstract

In order to achieve image denoising in high-noise environment, this paper proposes an image denoising algorithm based on improved wavelet thresholding algorithm.The algorithm first improves the problem of fixed threshold.Secondly, an improved wavelet threshold function is proposed for the traditional hard and soft threshold function.Finally, the combination of improved threshold function and threshold can improve the accuracy of wavelet threshold judgment, and realize the effective separation of image and noise. Experimental results show that the proposed algorithm can not only effectively remove image noise in noisy environment, but also obtain higher(Peak signal-to-noise ratio, PSNR) and smaller (Mean Squared Error, MSE).

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Funding

This study was supported by Academic Support Program for Top Talents in University Disciplines [Grant Number, gxbjZD2022115, gxbjZD2021065]; Anhui Polytechnic University—Jiujiang District industrial collaborative innovation New Special Fund Project [Grant Number, 2021cyxtb2]; Key Technology Project of Wuhu City [Grant Number, 2022hg11]; Anhui University Natural Science Research Project key project[Grant Number, KJ2021ZD0152].

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Authors and Affiliations

  1. School of Electrical Engineering, Anhui Polytechnic University, Wuhu, 241000, China

    Guowu Zhu & Bingyou Liu

  2. Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment, Ministry of Education, Wuhu, 241000, China

    Guowu Zhu & Bingyou Liu

  3. Anhui Dar Intelligent Control System Stock Co.,Ltd, Wuhu, 241000, China

    Pan Yang

  4. Wuhu HIT Robot Industrial Technology Research Institute Co., Ltd, Wuhu, 241000, China

    Xuan Fan

Authors
  1. Guowu Zhu
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  2. Bingyou Liu
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  3. Pan Yang
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  4. Xuan Fan
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All authors have made equal contributions.

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Correspondence to Bingyou Liu.

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Zhu, G., Liu, B., Yang, P. et al. Image denoising method based on improved wavelet threshold algorithm. Multimed Tools Appl 83, 67997–68011 (2024). https://doi.org/10.1007/s11042-024-18197-w

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