Yang Xu, Yu Zhongyang, Xu Lu, Hu Jiemin, Wu Long, Yang Chenghua, Zhang Wei, Zhang Jianlong, Zhang Yong
Opt Express. 2021 Aug 30;29(18):28388-28405. doi: 10.1364/OE.435276.
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.
由于结构简单、成像距离远且具有非局域成像特性，鬼成像在水下有源光学成像中得到了广泛应用。然而，水下环境的复杂性会大大降低鬼成像的成像质量。为解决这一问题，该研究提出了一种基于生成对抗网络的水下鬼成像方法。所提网络的生成器采用具有双跳跃连接和注意力模块的U-Net来提高重建质量。在网络训练过程中，总损失函数是加权对抗损失、感知损失和像素损失之和。实验和仿真结果表明，所提方法有效提高了水下鬼成像的目标重建性能。该方法推动了基于鬼成像技术的水下目标有源光学成像的进一步发展。
