Zhang Lei, Zheng Guiping, Zhang Kai, Wang Yongfeng, Chen Changming, Zhao Liting, Xu Jiquan, Liu Xinqing, Wang Liqing, Tan Yiqiu, Xing Chao
School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150090, China.
China State Construction International Holdings Limited, Hong Kong 999077, China.
Materials (Basel). 2022 Oct 20;15(20):7364. doi: 10.3390/ma15207364.
An adaptive image-processing method for CT images of asphalt mixture is proposed in this paper. Different methods are compared according to the error analysis calculated between the real gradation and 3D reconstruction gradation. As revealed by the test results, the adaptive image-processing method was effective in carrying out different brightness homogenization processes for each image. The Wiener filter with 7 × 7 size filter was able to produce a better noise reduction effect without compromising image sharpness. Among the three methods, the adaptive image-processing method performed best in the accuracy of coarse aggregate recognition, followed by the ring division method and the global threshold segmentation method. The error of the gradation extracted by the adaptive image-processing method was found to be lowest compared with the real gradation. For a variety of engineering applications, the developed method helps to improve the analysis of CT images of asphalt mixtures.
本文提出了一种用于沥青混合料CT图像的自适应图像处理方法。根据实测级配与三维重建级配之间的误差分析,对不同方法进行了比较。试验结果表明,自适应图像处理方法能够对每幅图像进行不同的亮度均匀化处理。尺寸为7×7的维纳滤波器在不影响图像清晰度的情况下,能够产生较好的降噪效果。在三种方法中,自适应图像处理方法在粗集料识别精度方面表现最佳,其次是环刀法和全局阈值分割法。与实测级配相比,自适应图像处理方法提取的级配误差最小。对于各种工程应用,所开发的方法有助于改进沥青混合料CT图像的分析。