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Ghost imaging is widely used in underwater active optical imaging because of its simple structure, long distance, and non-local imaging. However, the complexity of the underwater environment will greatly reduce the imaging quality of ghost imaging. To solve this problem, an underwater ghost imaging method based on the generative adversarial networks is proposed in the study. The generator of the proposed network adopts U-Net with the double skip connections and the attention module to improve the reconstruction quality. In the network training process, the total loss function is the sum of the weighted adversarial loss, perceptual loss, and pixel loss. The experiment and simulation results show that the proposed method effectively improves the target reconstruction performance of underwater ghost imaging. The proposed method promotes the further development of active optical imaging of underwater targets based on ghost imaging technology.

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Abstract-The electromagnetic scattering mechanism of radar targets in the high-frequency domain can be characterized exactly by geometrical theory of diffraction (GTD) model. In this paper, we propose a novel parameter estimation method for GTD model based on compressed sensing. The sparse characteristic of radar echoes is analyzed, and the parameter estimation problem is converted to one of sparse signal reconstruction. Furthermore, clustering and linear least-minimum-squares algorithms are utilized to improve the accuracy of the result. Compared with several modern spectral estimation techniques, the proposed method gives a more precise estimation of the GTD model parameters, especially the scattering centers. Simulations with synthetic and measured data in an anechoic chamber confirm the effectiveness of the method.