用于计算微波成像中遮挡目标的自适应阈值生成对抗重建
Generative Adversarial Reconstruction with Adaptive Thresholding for Obstructed Targets in Computational Microwave Imaging
浏览论文内容
中文总结 AI 辅助
该研究提出结合可学习软阈值模块的条件生成对抗网络框架，用于计算微波成像中遮挡目标的重建，在多数据集及不同场景下验证了其有效性与适应性。
中文摘要 AI 辅助
本研究提出一种端到端的计算微波成像（CMI）生成对抗框架，用于直接从被非期望物体遮挡的目标测量数据中重建感兴趣目标。该框架将条件生成对抗网络（cGAN）与可学习软阈值模块（STM）相结合，以自适应抑制非目标相关信息。在包含被E-MNIST字母遮挡的MNIST数字的多样化数据集上进行训练和测试，同时在实验CMI系统获取的测量数据上评估所提框架。此外，还对不同几何形状的物体开展进一步研究，证明所提方法可适配其他类型物体。数值实验表明，理想条件下所提cGAN-STM的归一化均方误差（NMSE）为0.066，结构相似性指数（SSIM）为0.876。还开展了基准测试、STM机制分析及不同遮挡尺寸下的评估等综合分析，评估了模型在不同信噪比（SNR）场景下的性能，15 dB SNR时实现合理的重建质量，NMSE为0.168，SSIM为0.700，即使在低SNR水平下也能保留可识别的目标轮廓，这些结果凸显了所提方法的有效性和适应性。
英文摘要
In this work, an end-to-end generative adversarial framework for computational microwave imaging (CMI) is proposed to reconstruct the targets of interest directly from the measurements of targets obstructed by undesired objects. It integrates a conditional generative adversarial network (cGAN) with a learnable soft-threshold module (STM) to adaptively suppress non-target related information. The proposed framework is evaluated on a diverse dataset comprising MNIST digits obstructed by E-MNIST letters for training and testing, as well as on measurements acquired with an experimental CMI system. In addition, further studies involving objects with different geometries are conducted, demonstrating that the proposed approach can be adapted to other types of objects. Numerical experiments show that the proposed cGAN-STM achieves a normalized mean square error (NMSE) of 0.066 and a structural similarity index (SSIM) of 0.876 under ideal conditions. Comprehensive analyses, including benchmarking, analysis of the STM mechanism, and evaluation under different obstruction sizes, are also conducted. The performance of the model under different signal-to-noise ratio (SNR) scenarios is also evaluated, achieving reasonable reconstruction quality at 15 dB SNR with an NMSE of 0.168 and an SSIM of 0.700. Even at low SNR levels, recognizable target outlines are preserved. These results highlight the effectiveness and adaptability of the proposed method.
