Imaging through scattering media is one of the hotspots in the optical field, and impressive results have been demonstrated via deep learning (DL). However, most of the DL approaches are solely data-driven methods and lack the related physics prior, which results in a limited generalization capability. In this paper, through the effective combination of the speckle-correlation theory and the DL method, we demonstrate a physics-informed learning method in scalable imaging through an unknown thin scattering media, which can achieve high reconstruction fidelity for the sparse objects by training with only one diffuser. The method can solve the inverse problem with more general applicability, which promotes that the objects with different complexity and sparsity can be reconstructed accurately through unknown scattering media, even if the diffusers have different statistical properties. This approach can also extend the field of view (FOV) of traditional speckle-correlation methods. This method gives impetus to the development of scattering imaging in practical scenes and provides an enlightening reference for using DL methods to solve optical problems.

中文翻译：

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基于物理知识的学习，通过未知的散射介质成像

通过散射介质成像是光学领域的热点之一，并且通过深度学习（DL）展示了令人印象深刻的结果。但是，大多数DL方法仅是数据驱动的方法，并且缺少相关的物理方法，这导致泛化能力有限。在本文中，通过斑点相关理论和DL方法的有效结合，我们演示了一种通过未知稀薄散射介质进行可伸缩成像的物理信息学习方法，该方法可以通过对稀疏对象进行训练而获得较高的重建保真度。只有一个扩散器。该方法可以解决反问题，具有更广泛的适用性，从而可以通过未知的散射介质准确地重建复杂度和稀疏度不同的物体，即使扩散器具有不同的统计属性。这种方法还可以扩展传统散斑相关方法的视场（FOV）。该方法为实际场景中散射成像的发展提供了动力，并为使用DL方法解决光学问题提供了启发性的参考。