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Image restoration for blurred fringes of rail profile 3D online measurement based on PMP
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摘要:
在线相位测量轮廓术(PMP)中，当被测物体运动速度较高时，所采集的变形条纹往往为运动模糊图像，使得复原误差增大，严重时可能导致三维重建无法进行。将在线PMP运用于钢轨外形及表面缺陷的在线三维测量时，为了实现钢轨表面模糊变形条纹的清晰化，本文对维纳滤波法、点扩散函数算法、盲解卷积算法和Richardson-Lucy算法等几种模糊图像复原算法进行了对比分析，用峰值信噪比对模糊条纹图像的复原效果进行评估。同时，研究了车辆运行速度和图像复原效果之间的关系，得出了复原效果与运行速度之间的关系曲线，进行了误差分析，并用在线PMP实现了钢轨外形的三维重建。理论及实验结果表明：在对钢轨外形轮廓及表面缺陷的在线三维测量时，Richardson-Lucy算法的复原效果最佳，图像复原程度与车辆运行速度呈多项式关系。
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关键词:
- 变形条纹 /
- 模糊图像复原 /
- Richardson-Lucy算法 /
- 峰值信噪比
Abstract:In online phase measurement profilometry (PMP), when the velocity of the object is quite high, the collected reformed fringes often appear to be motion-blurred images, which increases the measurement error, or even leads to the 3D reconstruction failure. In the online PMP 3D measurement of rail shape and surface flaw, in order to clarify the blurred reformed fringes of the rail, several restoration methods, such as Wiener Filtering, Point Spread Function algorithm, Blind Deconvolution algorithm, and Richardson-Lucy algorithm were compared and analyzed. And the peak signal to noise ratio (PSNR) is used for evaluating the restoration effect. Meanwhile, the relationship between the vehicle speed and the restoration effect was studied, the error was analyzed and the 3D rail shape was reconstructed based on online PMP. Theoretical and experimental results show that in the online 3D measurement of rail shape and surface flaw, the Richardson-Lucy algorithm is the best one for image restoration of motion blur, and the relationship between the effect of image restoration and the vehicle speed is a polynomial.
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Key words:
- deformed fringe /
- blurred image restoration /
- Richardson-Lucy algorithm /
- PSNR
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Abstract: In rail transportation, rail plays an important role in supporting and guiding the vehicle running. However, in the process of vehicle running, the friction and the pressure on the rail easily lead to wear and deformation. When rail wear and deformation reach a certain degree, it will bring potential dangers to the vehicle running. The measurement of rail shape and surface defects has become a focus in the railway industry. In recent years, 3D measurement techniques based on fringe projection have been introduced into online measurement of rail profile and surface defects. Phase measurement profilometry (PMP) is a widely-used method, which is suitable for 3D online measurement of rail with high precision and easy to measure online. In online PMP, when the velocity of the object is quite high, the collected reformed fringes often appear to be motion-blurred, which increases the measurement error, or even leads to the 3D reconstruction failure. In the 3D measurement of rail shape and surface flaw with online PMP, in order to clarify the blurred reformed fringes of the rail, several restoration methods, such as Wiener Filtering, Point Spread Function algorithm, Blind Deconvolution algorithm, and Richardson-Lucy algorithm were compared and analyzed. And the peak signal to noise ratio (PSNR) is used for evaluating the restoration effect. The PSNR is higher, the image quality is better. Meanwhile, the relationship between the vehicle speed and the restoration effect was studied, the error was analyzed and the 3D rail shape was reconstructed based on online PMP. Theoretical and experimental results show that in the online 3D measurement of rail shape and surface flaw, the Richardson-Lucy algorithm is best for image restoration of motion blur, and the relationship between the effect of image restoration and the vehicle speed is a polynomial. When online PMP method is used for reconstructing 3D motion blurred image, the 3D shape quality of the rail will be poor, and the 3D restoration of the reconstructed fringe images can get good 3D shape of the rail. In actual measurement, the obtained motion blurred fringe images are processed with Richardson-Lucy algorithm, and online PMP is used to reconstruct the 3D shape of the rail. In comparison with the 3D shape of the standard rail, the abrasion of the rail is obtained. If the rail surface is defective, the accurate position and size of the defect can be determined by the reconstruction of the 3D shape of the rail.
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图 4 各种算法的光栅条纹图像复原图. (a)维纳滤波法. (b)点扩散函数算法. (c)盲解卷积算法. (d) Richardson-Lucy算法.
Figure 4. Restored grating fringes images of various algorithms. (a) Wiener filtering method. (b) Point spread function algorithm. (c) Blind deconvolution algorithm. (d) Richardson-Lucy algorithm.
图 5 峰值信噪比与模糊核长度的关系. (a)模糊图像. (b)复原图像.
Figure 5. The relationship between peak signal-to-noise ratio and fuzzy kernel length. (a) Blurred images. (b) Restored images.
图 6 θ=10°时光栅条纹图像. (a)运动模糊图像. (b) Richardson-Lucy算法的复原图.
Figure 6. The grating fringes image when θ=10°. (a) Motion blurred image. (b) Restored image of the Richardson-Lucy algorithm.
图 7 复原图像的PSNR值与模糊核长度的关系(θ=10°).
Figure 7. The relationship between PSNR value and fuzzy kernel length of reconstructed image (θ=10°).
图 8 实验得到的光栅条纹图像. (a)静止拍摄条纹图. (b)运动模糊的条纹图. (c) RichardsonLucy算法的复原图.
Figure 8. Grating fringes images obtained by experiments. (a) A static shot of a fringes image. (b) Motion blurred fringes image. (c) Restored image of the Richardson -Lucy algorithm.
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