Foreground-target reconstruction in computational microwave imaging using a generative model

Although conventional microwave imaging techniques offer high-fidelity image reconstructions, they suffer from significant drawbacks, such as intensive hardware complexity. Computational microwave imaging (CMI)-based systems are often considered alternatives to address these challenges, but they are usually limited by the complexity of the computation layer due to the intensive computational demands for the reconstruction step. A variety of deep learning techniques have been proposed to enhance the computation efficiency, but existing studies primarily focus on scenarios where the imaged targets are isolated. To address this challenge, this work explores computational imaging scenarios involving targets of interest overlapping with secondary objects. This article designs a novel generative model to reconstruct the images of the objects in the foreground of overlapping imaging targets. Performance is evaluated using average normalized mean squared error (NMSE) and structural similarity index (SSIM). While conventional methods require manual removal of background objects for accurate foreground reconstructions in overlapped imaging, the proposed method achieves this by learning features directly from the measured signals, significantly improving the CMI computational efficiency.