A mine image denoising algorithm based on improved trimmed mean
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摘要: 现有矿井图像去噪算法对于复杂噪声的去除效果有限，且处理速度不能满足实时监控需求。针对该问题，提出一种基于改进切尾均值的矿井图像去噪算法。首先，采用切尾均值滤波器对图像噪声进行初步滤除，同时引入二次检验机制处理残留的噪声点，通过引入离散系数提升算法对不同像素的区分能力，增强去噪性能；其次，采用基于极值数量的分类处理及再次检验机制，有效减少残留噪声问题；然后，在小波函数中引入新的控制变量优化软阈值函数和硬阈值函数，构建双阈值函数，结合Radon变换增强对线性特征的处理，增强对矿井图像的检测能力；最后，采用均方误差（MSE）与峰值信噪比（PSNR）进行图像质量评价。实验结果表明：相较于切尾均值算法、硬阈值算法、软阈值算法，基于改进切尾均值的矿井图像去噪算法处理的图像的MSE增长相对缓慢，MSE最小，图像去噪效果最好；引入离散系数后，去噪图像的MSE相较于引入前低300 dB左右，PSNR相较于引入前高20 dB左右，引入离散系数能有效减少噪声点对算法的影响；相较于卡尔曼遗传优化算法、变换域图像去噪算法、交叉分支卷积去噪网络，基于改进切尾均值的矿井图像去噪算法处理的图像MSE分别降低了27，21，13 dB，PSNR分别提升了8，6，3 dB，去噪耗时分别缩短了0.20，0.16，0.14 s。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.
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图 2 小波变换与Radon变换融合流程
Figure 2. The fusion process of wavelet transform and Radon transform
图 4 不同算法去噪图像的MSE和PSNR
Figure 4. MSE and PSNR of denoised images by different algorithms
图 5 离散系数引入前后去噪图像的MSE和PSNR对比
Figure 5. Comparison of MSE and PSNR of denoised image before and after the introduction of discrete coefficients
表 1 不同算法实验结果对比
Table 1. Comparison of experimental results of different algorithms
算法 MSE/dB PSNR/dB 去噪耗时/s 卡尔曼遗传优化算法 277 53 3.11 变换域图像去噪算法 271 55 3.07 交叉分支卷积去噪网络 263 58 3.05 本文算法 250 61 2.91 
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