基于符号相干系数自适应加权的全聚焦成像

Total Focusing Method Based on Adaptive Weighting of Sign Coherence Factor

全聚焦成像是前沿的阵列超声成像检测算法,但易受到噪声和过程扰动的影响而降低成像质量.针对上述问题,本文将基于符号相干系数的自适应加权方法与全聚焦全矩阵成像算法相结合,提出TFM-SCF算法.利用带有常见人工缺陷的模拟试块进行验证试验,结果表明,该算法与原始TFM算法一样保持了对各种类型缺陷识别判读的准确性;且TFM-SCF算法的成像分辨力和对比度均优于原始TFM算法,经统计,横向分辨力的平均提升量为0.129 mm,纵向分辨力的平均提升量为0.160 mm,对比度CR的平均提高量为22.252dB,证明提出的基于符号相干系数的自适应加权方法可以有效地提升全聚焦的成像质量.

TFM is a cutting-edge array ultrasonic imaging detection algorithm,but it is easy to be affected by noise and process disturbance,which reduces the imaging quality.To solve the above problems,this paper combines the adaptive weighting method based on SCF with the TFM algorithm,and proposes TFM-SCF algorithm.The verification test is carried out through the simulation test block with common artificial defects.The results show that the algorithm maintains the accuracy of identifying and interpreting various types of defects as the original TFM algorithm;The imaging resolution and contrast of TFM-SCF algorithm are better than the original TFM algorithm.According to statistics,the average improvement of transverse resolution is 0.129 mm,the average improvement of longitudinal resolution is 0.160 mm,and the average improvement of contrast ratio(CR)is 22.252 dB.It is proved that the proposed adaptive weighting method based on SCF can effectively improve the imaging quality of TFM.