Generative model-based image reconstruction from back-scattered data in computational microwave imaging

Abstract

Conventional microwave imaging can provide high-quality reconstructed images, but is also limited by the increased hardware complexity and a slow data acquisition speed. Although computational imaging (CI)-based systems are developed to be alternatives, they may require substantial computational power and time. To reduce the hardware complexity and computational burden associated with scene reconstructions of CI applications, in this paper, a conditional generative adversarial network (cGAN) is presented to achieve image reconstruction, where the back-scattered measurement is regarded as both the condition and the input of the proposed network. With testing dataset, the average values of the normalized mean squared error (NMSE) and the normalized mean absolute error (NMAE) are 0.0474 and 0.2267, respectively. In addition, a noise analysis is conducted, showing the reliability of the proposed network in noisy settings.