Opt Express. 2023 Apr 24;31(9):14343-14357. doi: 10.1364/OE.484252.
Imaging through scattering media is a useful and yet demanding task since it involves solving for an inverse mapping from speckle images to object images. It becomes even more challenging when the scattering medium undergoes dynamic changes. Various approaches have been proposed in recent years. However, none of them are able to preserve high image quality without either assuming a finite number of sources for dynamic changes, assuming a thin scattering medium, or requiring access to both ends of the medium. In this paper, we propose an adaptive inverse mapping (AIP) method, which requires no prior knowledge of the dynamic change and only needs output speckle images after initialization. We show that the inverse mapping can be corrected through unsupervised learning if the output speckle images are followed closely. We test the AIP method on two numerical simulations: a dynamic scattering system formulated as an evolving transmission matrix and a telescope with a changing random phase mask at a defocused plane. Then we experimentally apply the AIP method to a multimode-fiber-based imaging system with a changing fiber configuration. Increased robustness in imaging is observed in all three cases. AIP method's high imaging performance demonstrates great potential in imaging through dynamic scattering media.
通过散射介质进行成像是一项有用但具有挑战性的任务，因为它涉及从散斑图像到物体图像的逆映射求解。当散射介质发生动态变化时，情况会更加复杂。近年来，已经提出了各种方法。然而，它们都无法在不假设动态变化的有限数量的光源、假设薄散射介质或需要访问介质两端的情况下，保持高质量的图像。在本文中，我们提出了一种自适应逆映射（AIP）方法，该方法不需要对动态变化的先验知识，只需要在初始化后输出散斑图像。我们表明，如果输出的散斑图像紧密跟随，那么通过无监督学习可以纠正逆映射。我们在两个数值模拟中测试了 AIP 方法：一个动态散射系统被表示为一个不断演变的传输矩阵，另一个是在离焦平面具有变化的随机相位掩模的望远镜。然后，我们在一个具有变化光纤配置的基于多模光纤的成像系统中实验应用 AIP 方法。在所有三种情况下，成像的稳健性都得到了提高。AIP 方法的高成像性能证明了其在动态散射介质成像中的巨大潜力。
