Imaging through scattering media is valuable for many areas, such as biomedicine and communication. Recent progress enabled by deep learning (DL) has shown superiority especially in the model generalization. However, there is a lack of research to physically reveal the origin or define the boundary for such model scalability, which is important for utilizing DL approaches for scalable imaging despite scattering with high confidence. In this paper, we find the amount of the ballistic light component in the output field is the prerequisite for endowing a DL model with generalization capability by using a “one-to-all” training strategy, which offers a physical meaning invariance among the multisource data. The findings are supported by both experimental and simulated tests in which the roles of scattered and ballistic components are revealed in contributing to the origin and physical boundary of the model scalability. Experimentally, the generalization performance of the network is enhanced by increasing the portion of ballistic photons in detection. The mechanism understanding and practical guidance by our research are beneficial for developing DL methods for descattering with high adaptivity.

中文翻译：

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通过散射介质可缩放成像的物理起源和边界：基于深度学习的探索

通过散射介质成像对许多领域都很有价值，例如生物医学和通信。深度学习 (DL) 带来的最新进展已显示出优势，尤其是在模型泛化方面。然而，缺乏研究来物理揭示起源或定义这种模型可扩展性的边界，这对于利用 DL 方法进行可扩展成像很重要，尽管散射具有很高的置信度。在本文中，我们发现输出场中弹道光分量的数量是通过使用“one-to-all”训练策略赋予 DL 模型泛化能力的先决条件，这提供了多源之间的物理意义不变性。数据。这些发现得到了实验和模拟测试的支持，在这些测试中，分散和弹道组件的作用揭示了模型可扩展性的起源和物理边界。在实验上，通过增加检测中的弹道光子部分来增强网络的泛化性能。我们研究的机制理解和实践指导有利于开发具有高适应性的 DL 去散射方法。