Conventional microwave imaging-based approaches can produce high quality image reconstructions. At the same time, these techniques typically suffer from increased hardware complexity, cost and slow data acquisition speeds. Although computational imaging (CI)-based systems have been developed as an alternative, they may demand significant computational power and time, especially in the calculation and the storage of the transfer function (or the sensing matrix) of the CI system. However, the previous method considers the scenario where the transmitter and receiver share the same set of aperture distribution fields. To address this challenge, this paper presents a new technique, where the sensing matrix is calculated directly from the aperture fields of the antennas in a CI system. Here, the transmitter and the receiver apertures can be different and they do not necessarily need to have the same field distributions. With the testing dataset, the average value of the normalized mean squared error (NMSE) is 0.0243. In addition, compared to the traditional method, the proposed network reduces the computation time for the sensing matrix by approximately 67%. The proposed network can predict the sensing matrix from two different sets of aperture distribution fields with high accuracy while significantly saving the computation time.
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.
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