基于区域特征脉冲耦合神经网络的航空发动机涡轮叶片DR图像融合

DR Image Fusion of Aero‑engine Turbine Blades Based on Regional Feature Pulse Coupled Neural Network

针对大厚比的复杂结构件数字射线成像(Digital radiography,DR),单一透照能量不能完整体现全部信息的问题,提出一种基于区域特征的脉冲耦合神经网络(Pulse coupled neural network,PCNN)多幅图像融合算法。以航空发动机涡轮叶片为研究对象,首先在获取多幅递增管电压透照子图基础上,经非下采样轮廓波变换(Non⁃subsampled contourlet transform,NSCT)分解为一个低频子带和多个尺度下的高频子带;其次采用PCNN算法,用各子带的改进空间频率中方向特征最明显的分量调整连接强度;然后低频子带采用区域均方差、高频子带采用改进的拉普拉斯能量和作为外部激励,点火映射图的判决遵循取大原则;最后通过NSCT逆变换得到融合结果图。实验结果表明,以熵、标准差、平均梯度、清晰度和空间频率作为客观评价指标,与基于拉普拉斯金字塔变换等经典融合算法相比均有所提升。本文研究方法性能优越,丰富了融合图像的细节信息,可获得更高质量的DR融合图像。

Aiming at the problem that single transillumination energy cannot completely cover all the information for the digital radiography(DR)of complex structures with large thickness ratios,we propose a pulse coupled neural network(PCNN)image fusion algorithm based on regional characteristics and take aero-engine turbine blades as the research objects.First,the multiple incremental tube voltage transillumination sub-images are decomposed into low frequency sub-bands and high frequency sub-bands at multiple scales by the non-sub-sampled contourlet transform(NSCT).Second,the PCNN algorithm is deployed to adjust the connection strength of the directions that hold the most obvious characteristics in the improved spatial frequency of each sub-band.Third,to fulfill the external excitation,the low-frequency sub-bands are calculated by the regional mean square error,while the high-frequency sub-band by the summodified Laplacian.Thus the two results are processed through the fire mapping by following the maximum principle.Finally,the fusion images are obtained by the NSCT inverse transformation.The experimental results show that the proposed method can improve fusion results in terms of entropy,standard deviation,average gradient,clarity and spatial frequency,compared with classical fusion algorithms including the methods based on the Laplace pyramid transformation.Our method can extend image-fusion performance by enriching the detailed information of the images and obtaining higher quality.