Peng Yang, Chen Wen
Opt Lett. 2023 Sep 1;48(17):4480-4483. doi: 10.1364/OL.499787.
In this Letter, we propose a learning-based correction method to realize ghost imaging (GI) through dynamic scattering media using deep neural networks with Gaussian constraints. The proposed method learns the wave-scattering mechanism in dynamic scattering environments and rectifies physically existing dynamic scaling factors in the optical channel. The corrected realizations obey a Gaussian distribution and can be used to recover high-quality ghost images. Experimental results demonstrate effectiveness and robustness of the proposed learning-based correction method when imaging through dynamic scattering media is conducted. In addition, only the half number of realizations is needed in dynamic scattering environments, compared with that used in the temporally corrected GI method. The proposed scheme provides a novel, to the best of our knowledge, insight into GI and could be a promising and powerful tool for optical imaging through dynamic scattering media.
在本信函中，我们提出一种基于学习的校正方法，通过具有高斯约束的深度神经网络，实现透过动态散射介质的鬼成像（GI）。所提方法学习动态散射环境中的波散射机制，并校正光通道中实际存在的动态缩放因子。校正后的实现服从高斯分布，可用于恢复高质量的鬼图像。实验结果证明了所提基于学习的校正方法在透过动态散射介质进行成像时的有效性和鲁棒性。此外，与时间校正鬼成像方法相比，在动态散射环境中仅需要一半数量的实现。据我们所知，所提方案为鬼成像提供了一种新颖的见解，并且可能成为透过动态散射介质进行光学成像的一种有前途且强大的工具。
