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基于改进切尾均值的矿井图像去噪算法

熊增举 姚成贵 张德华

熊增举,姚成贵,张德华. 基于改进切尾均值的矿井图像去噪算法[J]. 工矿自动化,2024,50(4):63-68.  doi: 10.13272/j.issn.1671-251x.2024010063
引用本文: 熊增举,姚成贵,张德华. 基于改进切尾均值的矿井图像去噪算法[J]. 工矿自动化,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
Citation: 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

基于改进切尾均值的矿井图像去噪算法

doi: 10.13272/j.issn.1671-251x.2024010063
基金项目: 江西省教育厅科学技术研究项目(GJJ204501);浙江省自然科学基金探索项目(Y24A050006)。
详细信息
    作者简介:

    熊增举(1982—),男,青海海东人,副教授,研究方向为电子信息科学与技术、软件工程,E-mail:xiongzengju1982@163.com

  • 中图分类号: TD67

A mine image denoising algorithm based on improved trimmed mean

  • 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。

     

  • 图  1  改进切尾均值算法流程

    Figure  1.  The process of improved tail cut mean algorithm

    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  3  不同算法的去噪效果

    Figure  3.  The denoising effect of different algorithms

    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

    图  6  不同算法的去噪耗时

    Figure  6.  Time consumption of denoising of different algorithms

    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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出版历程
  • 收稿日期:  2024-01-19
  • 修回日期:  2024-04-18
  • 网络出版日期:  2024-05-10

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