Enhancing ISAR Resolution by a Generative Adversarial Network
Dan Qin, Xunzhang Gao
Abstract
Recent studies have shown the superiority of neural networks on imaging equality and efficiency in inverse synthetic aperture radar (ISAR) resolution enhancement, but a central problem remains largely unsolved: all recent studies based on neural networks focused on minimizing the mean-squared reconstruction error (MSE), causing limited enhancing factors and inaccurate recovery of weak point scatters. In order to address this problem, a framework based on a generative adversarial network (GAN) using a combined loss composed of the absolute loss and the adversarial loss is proposed in this letter. The absolute loss ensures that reconstructed high-resolution ISAR images achieve higher enhancing factors and lower sidelobes. The adversarial loss pushes this framework to recover accurate amplitude and position of weak point scatters by a discriminator that is trained to differentiate reconstructed high-resolution ISAR images and real high-resolution ISAR images. Compared to some state-of-the-art methods, our GAN-based framework provides superior reconstruction with higher enhancing factors and more target details.