光谱学与光谱分析, 2017, 37 (12): 3683, 网络出版: 2018-01-04
小波图像融合在太赫兹无损检测中的应用
Wavelet-Based Image Fusion Method Applied in the Terahertz Nondestructive Evaluation
小波图像融合 太赫兹成像 无损检测 玻璃纤维增强复合材料 Wavelet-based image fusion Terahertz imaging Nondestructive detection Glass fiber-reinforced polymer composites
摘要
玻璃纤维增强复合材料被广泛应用于航天、 航空及其他军民各领域, 它在制备和使用过程中通常会出现多个缺陷。 太赫兹时域光谱成像技术有望成为玻璃纤维增强复合材料无损检测的有力补充手段。 在太赫兹时域光谱成像过程中, 可以选取时域或频域波形中的不同参数来进行成像。 对于不同的缺陷, 能够有效地对其进行检测的参数是不一样的。 采用基于小波分解的图像融合方法将多幅不同参数获取的太赫兹反射图像结合起来, 得到一幅包含所有缺陷信息的新图像。 研究表明, 基于小波分解的图像融合方法在太赫兹无损检测中的应用, 能够获取单一参数成像无法检测的缺陷信息, 为复合材料太赫兹图像后期处理提供了新的技术方法。
Abstract
Glass fiber-reinforced polymer (GFRP) composites are widely used in aerospace, aviation and other military and civilian fields. However, GFRP composites may have several defects due to the damage caused by various factors during the manufacturing processes and in deployment. Terahertz (THz) time-domain spectroscopy (TDS) imaging technology has the potential to become a powerful complement of the traditional nondestructive evaluation (NDE) methods for GFRP composites. During the imaging process, different parameters in time domain or frequency domain can be chosen to carry out the imaging. Targeting at different defects, the parameters which can effectively detect the defects are not the same. In this paper, the wavelet-based image fusion method is used to effectively combine multiple THz reflection images based on different parameters, and a new image which contains all the defects can be obtained. As shown in the experiments, due to the application of the wavelet-based image fusion method in the THz NDE, the defect information can be detected completely, which cannot be realized by single parameter imaging. It provides a new technical method for the post processing of the THz images of composite materials.
张瑾, 王洁, 沈雁, 张金波, 崔洪亮, 施长城. 小波图像融合在太赫兹无损检测中的应用[J]. 光谱学与光谱分析, 2017, 37(12): 3683. ZHANG Jin, WANG Jie, SHEN Yan, ZHANG Jin-bo, CUI Hong-liang, SHI Chang-cheng. Wavelet-Based Image Fusion Method Applied in the Terahertz Nondestructive Evaluation[J]. Spectroscopy and Spectral Analysis, 2017, 37(12): 3683.