A mine image denoising algorithm based on improved trimmed mean

XIONG Zengju; YAO Chenggui; ZHANG Dehua

doi:10.13272/j.issn.1671-251x.2024010063

Volume 50 Issue 4

Mar. 2024

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Journal of Mine Automation > 2024 > 50(4): 63-68. > DOI: 10.13272/j.issn.1671-251x.2024010063

XIONG Zengju, YAO Chenggui, ZHANG Dehua. A mine image denoising algorithm based on improved trimmed mean[J]. Journal of Mine Automation，2024，50（4）：63-68. DOI: 10.13272/j.issn.1671-251x.2024010063

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A mine image denoising algorithm based on improved trimmed mean

1.
Department of Electronic Engineering, Jiangxi Vocational and Technical College of Information Application, Nanchang 330043, China
2.
College of Data Science, Jiaxing University, Jiaxing 314000, China
3.
College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

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Received Date: January 18, 2024
Revised Date: April 17, 2024
Available Online: May 09, 2024

Abstract

Abstract

The existing mine image denoising algorithms have limited effectiveness in removing complex noise, and their processing speed cannot meet the requirements of real-time monitoring. In order to solve the above problems, a mine image denoising algorithm based on improved trimmed mean is proposed. Firstly, a trimmed mean filter is used to preliminarily filter out image noise, and a secondary inspection mechanism is introduced to handle residual noise points. By introducing discrete coefficients, the algorithm's capability to distinguish different pixels is improved, enhancing the denoising performance. Secondly, a classification processing and retesting mechanism based on the number of extreme values is adopted to effectively reduce the problem of residual noise. Thirdly, new control variables are introduced into the wavelet function to optimize the soft threshold function and hard threshold function, and a dual threshold function is constructed. The method combines with Radon transform to enhance the processing of linear features and enhance the detection capability of mine images. Finally, mean square error (MSE) and peak signal-to-noise ratio (PSNR) are used for image quality evaluation. The experimental results show that compared to the trimmed mean algorithm, hard threshold algorithm, and soft threshold algorithm, the MSE growth of the mine image denoising algorithm based on the improved trimmed mean is relatively slow, with the smallest MSE and the best image denoising effect. After introducing the discrete coefficient, the MSE of the model is about 300 dB lower than before, and the PSNR is about 20 dB higher than before. Introducing the discrete coefficient can effectively reduce the impact of noise points on the algorithm. Compared with Kalman genetic optimization algorithm, transform domain image denoising algorithm, and cross branch convolutional denoising network, the MSE of the proposed algorithm is reduced by 27, 21, and 13 dB respectively. The PSNR is improved by 8, 6, and 3 dB respectively. The time consumption is shortened by 0.20, 0.16, and 0.14 seconds, respectively.
- mine image denoising,
- trimmed mean,
- secondary inspection mechanism,
- wavelet transform,
- discrete coefficient,
- double threshold function,
- Radon transform

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References

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ZHANG Dehua

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College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China

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A mine image denoising algorithm based on improved trimmed mean