Cien Zhang, Jiaming Zhang, Jiajun He et al.
Computational microwave imaging (CMI) has gained attention as an alternative technique for conventional microwave imaging techniques, addressing their limitations such as hardware-intensive physical layer and slow data collection acquisition speed to name a few. Despite these advantages, CMI still encounters notable computational bottlenecks, especially during the image reconstruction stage. In this setting, both image recovery and object classification present significant processing demands. To address these challenges, our previous work introduced ClassiGAN, which is a generative deep learning model designed to simultaneously reconstruct images and classify targets using only back-scattered signals. In this study, we build upon that framework by incorporating attention gate modules into ClassiGAN. These modules are intended to refine feature extraction and improve the identification of relevant information. By dynamically focusing on important features and suppressing irrelevant ones, the attention mechanism enhances the overall model performance. The proposed architecture, named Att-ClassiGAN, significantly reduces the reconstruction time compared to traditional CMI approaches. Furthermore, it outperforms current advanced methods, delivering improved Normalized Mean Squared Error (NMSE), higher Structural Similarity Index (SSIM), and better classification outcomes for the reconstructed targets.