基于SRGAN的介电材料缺陷微波检测图像稀疏重建

SRGAN-based sparse reconstruction of defects in dielectric materials for microwave detection

介电材料在制造与服役过程中,可能由于制造瑕疵或复杂的服役环境,会出现脱黏、分层、材料损失等缺陷。微波无损检测是评估介电材料结构完整性的有效手段,但高分辨率成像需求与数据处理难度、检测效率之间的矛盾亟待解决。图像稀疏重建算法为解决此问题提供了可能,其中SRGAN(Super-resolution generative adversarial network)模型在稀疏重建方面表现优异。采用SRGAN进行微波成像稀疏重建,并针对微波图像的特性对SRGAN网络进行了改进,通过对比分析验证了改进后的算法在介电材料缺陷稀疏重建中的有效性。试验结果表明,改进后的SRGAN能够显著提高微波图像成像质量,准确还原缺陷细节,为介电材料结构的安全评估和使用寿命预测提供了有力支持。

Dielectric materials may occur during the manufacturing and service process due to manufacturing defects or complex service environments,which seriously affects the performance,microwave nondestructive testing is an effective means to assess the structural integrity of dielectric materials,but the contradiction between the demand for high-resolution imaging and the difficulty of data processing and detection efficiency needs to be resolved.Image sparse reconstruction algorithms provide a possibility to solve this problem,among which SRGAN(Super-Resolution Generative Adversarial Network)model is excellent in sparse reconstruction.In this paper,SRGAN was used for microwave imaging sparse reconstruction,and the SRGAN network was improved for the characteristics of microwave images,and the effectiveness of the improved algorithm in the sparse reconstruction of defects in dielectric materials was verified through comparative analysis.The results showed that the improved SRGAN can significantly improved the microwave image quality and accurately restore the defect details,which provides strong support for the safety assessment and service life prediction of dielectric material structures.