Sensing Matrix Prediction from Back-scattered Data in Computational Microwave Imaging

In this paper, the challenge of enhancing the efficiency of computational imaging (CI) at microwave frequencies is addressed. While CI simplifies the hardware complexity of conventional microwave imaging techniques, it requires the knowledge of a sensing matrix that is governed by the aperture radiated fields. This can be a computationally expensive process. As a drastic alternative to this conventional approach, a Pix2pix conditional generative adversarial network (cGAN) is introduced to learn the intricate relationship between the back-scattered measurements from the imaging scene and the sensing matrix of the imaging system. The proposed network yields high-fidelity estimations with minimized error and achieves a remarkable reduction in the time required to compute the sensing matrix. This advancement holds significant potential for improving the overall efficiency of microwave CI techniques, addressing both hardware complexity and computational burdens.