峰值信噪比标准下轨道图像预处理方法研究
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摘要
针对轨道检测图像分割慢和识别不精确的问题,提出了一种经过中值滤波和均衡化后调整差分计算的边缘检测预处理算法。首先使用均值、中值、维纳和SNN等4种去噪方法分别处理同一幅添加椒盐和高斯噪声的图像;然后使用3×3、5×5、7×7、9×9等4个模板分别对图像均衡化;最后在对Sobel增添45°、135°、-135°和-45°等四个梯度算子的基础上,求和算子内的计算点后均分255位的像素,再差分计算,并通过公式推导对比调整差分算子前后的抗噪能力。结果表明:(1)经过中值滤波的图像峰值信噪比最大,处理时间最短;(2)经过3×3增强后的图像PSNR最大;(3)经过该方法预处理后的轨道图像,峰值信噪比较大,原有轮廓信息保留度较高,能为后续快速分割和精确识别奠定基础。
A preprocessing algorithm of edge detection based on median filtering and equalization and adjustment of differential calculation is proposed to solve the problem of slow segmentation and inaccurate recognition of track image detection. Firstly, the same image with salt and Gaussian noise is processed by four denoising methods such as mean, median, Wiener and SNN; then, the image is equalized by using four templates of 3 × 3, 5 × 5, 7 × 7, 9 × 9 respectively; finally, On the basis of adding four gradient operators to Sobel, such as 45°, 135°,-135° and-45°, the calculation points in the summation operator are equally divided into 255-bit pixels, and then calculate difference,and the Anti-noise ability before and after adjusting the difference operator is deduced by formula. The results show that:(1) The peak signal-to-noise ratio(PSNR) of the image after median filtering is the largest and the processing time is the shortest;(2) The PSNR of the image after 3 × 3 enhancement is the largest;(3) The track image preprocessed by this method has a high PSNR and a high retention of the original contour information, which can lay a foundation for subsequent fast segmentation and accurate recognition.
A preprocessing algorithm of edge detection based on median filtering and equalization and adjustment of differential calculation is proposed to solve the problem of slow segmentation and inaccurate recognition of track image detection. Firstly, the same image with salt and Gaussian noise is processed by four denoising methods such as mean, median, Wiener and SNN; then, the image is equalized by using four templates of 3 × 3, 5 × 5, 7 × 7, 9 × 9 respectively; finally, On the basis of adding four gradient operators to Sobel, such as 45°, 135°,-135° and-45°, the calculation points in the summation operator are equally divided into 255-bit pixels, and then calculate difference,and the Anti-noise ability before and after adjusting the difference operator is deduced by formula. The results show that:(1) The peak signal-to-noise ratio(PSNR) of the image after median filtering is the largest and the processing time is the shortest;(2) The PSNR of the image after 3 × 3 enhancement is the largest;(3) The track image preprocessed by this method has a high PSNR and a high retention of the original contour information, which can lay a foundation for subsequent fast segmentation and accurate recognition.
引文
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[3]范宏.基于图像的铁路扣件缺陷检测算法研究[D].成都:西南交通大学, 2012.
[4]王昆,郑树彬,柴晓冬,等.轨道扣件几何特征提取算法研究[J].铁道科学与工程学报, 2015, 12(6):1 458–1 463.
[5]王昆,郑树彬.新的轨道特征提取算法[J].计算机系统应用, 2016, 25(2):162–166.
[6]李立明,柴晓冬,郑树彬.基于图像处理的轨道轮廓提取算法[J].上海工程技术大学学报, 2009, 23(2):170–173.
[7]丁锐.铁路图像分类和轨顶边缘定位算法研究[D].成都:西南交通大学, 2013.
[8]杭元元.基于计算机视觉的铁路扣件检测算法研究[D].成都:西南交通大学, 2014.
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[10]李彧雯.轨道部件图像检测系统设计[D].南昌:南昌大学, 2012.
[11]马文伟,赵永强,张国华,等.基于多结构元素形态滤波与自适应阈值分割相结合的红外弱小目标检测[J].光子学报, 2011, 40(7):1 020–1 024.
[12]徐岩,雷涛.基于形态学方法的车道线检测算法研究[J].铁道学报, 2009, 31(1):107–110.
[13]陈金胜,陈兴杰,彭乐乐,等.基于边缘特征的无砟轨道扣件定位方法[J].计算机测量与控制, 2018, 26(5):172–175.
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