线结构光焊接图像去噪方法

马增强; 钱荣威; 许丹丹; 杜巍

doi:10.12073/j.hjxb.20200519002

线结构光焊接图像去噪方法

马增强^{1, 2,},
钱荣威¹,
许丹丹¹,
杜巍^1, ,

1.
石家庄铁道大学，石家庄，050043
2.
石家庄铁道大学，省部级交通工程结构力学行为与体系国家重点实验室，石家庄，050043

基金项目: 国家自然科学基金资助项目(12072207)；河北省高等学校科学技术研究项目青年基金项目(QN2020155)；河北省研究生专业学位教学案例库项目(KCJSZ2019057).

详细信息

作者简介:
马增强，博士，教授，博士研究生导师；主要从事轴承故障诊断及图像识别等工作；Email：mzqlunwen@126.com.

通讯作者:
杜巍，博士，讲师；E-mail：duwei571@126.com.

中图分类号: TG 409
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出版历程
- 收稿日期: 2020-05-18
- 网络出版日期: 2021-02-05
- 刊出日期: 2021-02-24

Denoising of line structured light welded seams image based on adaptive top-hat transform

MA Zengqiang^{1, 2,},
QIAN Rongwei¹,
XU Dandan¹,
DU Wei^1, ,

1.
Shijiazhuang Tiedao University, Shijiazhuang, 050043, China
2.
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang, 050043, China

摘要

摘要: 为了滤除焊接过程中大量散射及飞溅焊渣等噪声，提出了一种基于自适应顶帽变换(adaptive Top-Hat transform)的线结构光焊接图像去噪方法. 将噪声图像进行一定范围结构元尺寸的Top-Hat变换处理，提出了一种评价指标互相关系数(cross-correlation coefficient of image histogram, CCIH)选取得到最优结构元尺寸L. 其次，进行一定迭代次数范围的顶帽变换(Top-Hat)变换处理，并提出一种基于最大类间方差(Otsu)改进结构相似度(SSIM)算法的评价指标相亮比(ratio of structural similarity index to average brightness，RSB)选取得到最优迭代次数I. 结果表明，与自适应中值法(median filter，MF)、全变分法(total variation，TV)和非下采样轮廓波变换与全变差法相结合的方法(non-subsampled contourlet transform with total variation，NSCT-TV)相比，该方法在主观视觉效果，信息熵(entropy，EN)、峰值信噪比(peak signal-to-noise ratio，PSNR)和均方根误差(mean-square error，MSE)上均有较大的改善. 噪声得到有效去除的同时图像中的线结构光区域得到较好的保留.
- 顶帽变换 /
- 焊接图像去噪 /
- 互相关系数 /
- Otsu算法 /
- 结构相似度
Abstract: Due to the scattering of the welding material, surface shape and laser line, the brightness distribution of the line structure is uneven and there are a lot of scattering noise around it, which affects the subsequent feature point extraction. In order to filter out the noise of the weld image of line structured light, a denoising model of weld image based on adaptive Top-Hat transformation is proposed. First, the noise image is transformed to a certain range of structural element size by Top-hat transform, and then an evaluation index was proposed, cross-correlation coefficient of image histogram (CCIH), and the optimal structural element size L is selected. Second, on the premise of the optimal structural element size L, the noise image is processed by the top-hat transform with a certain range of iterations times, a new model based on Otsu proposed SSIM model is proposed to select the optimal iterations time I from the evaluation index, Ratio of Structural Similarity Index to Average Brightness (RSB). Experimental results show that, compared with the method of Median Filter (MF), the method of Total Variation (TV), and the method combining Non-Subsampled Contourlet Transform with Total Variation (NSCT-TV), the proposed method has a great improvement in the aspects of subjective visual effect, entropy (EN), peak signal-to-noise ratio (PSNR) and mean-square error (MSE). Noise is suppressed more effectively and the line structured light area is preserved better in the image.
- top-hat transformation /
- welding image denoising /
- cross correlation coefficient /
- Otsu algorithm /
- structural similarity

HTML全文

图 1 CCIH与L关系图

Figure 1. Graph of CCIH and L

下载: 全尺寸图片幻灯片

图 2 L = 9与其他尺寸处理效果对比

Figure 2. Comparison of L = 9 and other sizes. (a) source image; (b) L = 3; (c) L = 9 (proposed); (d) L = 11; (e) L = 15; (f) L = 21

下载: 全尺寸图片幻灯片

图 3 不同L下SSIM与I的计算关系

Figure 3. Graph of SSIM and I under different L

下载: 全尺寸图片幻灯片

图 4 RSB与I的计算关系图

Figure 4. Graph of RSB and I

下载: 全尺寸图片幻灯片

图 5 I = 5与其他迭代次数处理效果对比

Figure 5. Comparison of I = 5 and other parameters. (a) source image; (b) I = 1; (c) I = 3; (d) I= 5 (proposed); (e) I = 15; (f) I = 20

下载: 全尺寸图片幻灯片

图 6 试验图像

Figure 6. Images of experiments. (a) No. 110; (b) No. 151; (b) No. 219; (d) No .239

下载: 全尺寸图片幻灯片

图 7 算法模型参数

Figure 7. Parameters of the proposed model. (a) L; (b) I

下载: 全尺寸图片幻灯片

图 8 去噪效果主观对比

Figure 8. Subjective comparison of denoising effect. (a) MF; (b) TV; (c) NSCT; (d) proposed

下载: 全尺寸图片幻灯片

表 1 去噪效果客观指标对比

Table 1 Comparison of objective indicators of denoising effect

焊接图像	评价指标	原始图像	自适应中值(MF)	全变分(TV)^[20]	非下采样全变分(NSCT-TV)^[21]	文中算法
第一组	EN	7.593 6	7.077 3	7.080 5	7.064 1	4.014 3
	PSNR	−	9.935 5	16.067 3	16.655 1	36.935 5
	MSE	−	1 429.728 8	153.342 7	133.930 4	62.929 0
第二组	EN	7.627 8	7.136 8	7.040 2	6.902 5	4.095 6
	PSNR	−	9.923 2	13.941 7	14.035 2	34.923 2
	MSE	−	1248.230 3	252.187 7	146.817 3	63.615 37
第三组	EN	8.247 6	7.219 9	7.205 1	7.201 8	4.299 1
	PSNR	−	7.769 7	13.969 5	18.154 5	38.264 3
	MSE	−	1 086.774 1	126.945 6	126.069 2	9.697 4
第四组	EN	8.240 0	7.217 3	7.245 3	7.222 7	4.103 9
	PSNR	−	6.797 3	19.281 0	19.763 2	37.479 6
	MSE	−	1359.404 1	767.319 1	686.695 5	11.617 8

下载: 导出CSV

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