基于小波变换的扩散焊超声 C 图像融合算法-《郑州大学学报(工学版)》

文章信息/Info

Title:: Ultrasonic C Image Fusion Algorithm for Diffusion Welding Based on Wavelet Transform

作者:: 常青¹; 杨程伟¹; 罗彬杰¹; 李细峰²; 1.华东理工大学信息科学与工程学院,上海 200237, 2.上海交通大学塑性成形技术与装备研究院,上海 200030

Author(s):: CHANG Qing¹; YANG Chengwei¹; LUO Binjie¹; LI Xifeng²; 1.School of Information Science and Engineering, 2.East China University

Keywords:: diffusion welding; ultrasound C scan; wavelet transform; guided filtering; image fusion

摘要:: 为了提高钛合金扩散焊界面微小缺陷的检测能力,提出基于小波变换的超声 C 图像融合算法, 综合多幅图像中的缺陷信息,重构出包含微小缺陷在内的超声 C 图像。首先;利用小波变换将待融合的超声 C 图像分解为低频和高频部分,并根据高、低频系数的不同特征,对高、低频系数进行差异化融合处理,对融合后的系数使用小波逆变换得到融合图像;最后,为了提高图像对比度、丰富图像细节信息,使用改进的同态滤波器对融合图像进行增强,从而获得结果图像。制备不同类型的人工缺陷试样进行测试,并将所提算法和常规超声 C 扫描检测的缺陷长度进行定量比较。实验结果表明:对于线形微小缺陷和弱结合缺陷,使用所提算法重构的超声 C 图像的平均误差分别为 2 mm 和 4. 2 mm,常规超声 C 扫描的平均误差分别为 8 mm 和 9. 5 mm。所提算法重构的超声 C 图像能够更为准确地反映出缺陷信息。

Abstract:: In order to improve the detection ability of tiny defects at the interface of diffusion welding of titanium alloys, an ultrasonic C image fusion algorithm based on wavelet transform was proposed. Firstly, the ultrasonic C image to be fused is decomposed into low-frequency and high-frequency parts by wavelet transform, and the high and low frequency coefficients are differentiated and fused according to the different characteristics of the high and low frequency coefficients, and the inverse wavelet transform is used for the fused coefficients to get the fused image. Finally, in order to improve the contrast of the image and enrich the details of the image, the improved homomorphic filter is used to enhance the fusion image, so as to obtain the fusion result image. By preparing different types of artificial defect samples for testing, and quantitatively comparing the length of defects detected by the algorithm in this paper and conventional ultrasonic C scan, the experimental results show that for small linear defects and weak bond defects, the ultrasonic C reconstructed by the algorithm in this paper can be used. The average errors of the images were 2 mm and 4. 2 mm, respectively, and the average errors of conventional ultrasound C scan was 8 mm and 9. 5 mm, respectively. Therefore, the ultrasonic C image reconstructed by the proposed algorithm in this paper can reflect the defect information more accurately.