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arXiv 2403.12970eess.IVcs.CVphysics.bio-phphysics.optics

用于可编程照明计算显微术的混合深度学习与基于物理的神经网络

Hybrid deep learning and physics-based neural network for programmable illumination computational microscopy

School of Optics and Photonics, Beijing Institute of Technology（北京理工大学光电学院）；Changchun University of Science and Technology（长春理工大学）

Ruiqing Sun, Delong Yang, Shaohui Zhang, Qun Hao

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AI总结：

针对可编程照明计算显微术中的逆样品重建问题，提出一种结合深度学习与物理模型的混合神经网络框架，通过融合语义信息与物理约束提升成像质量与泛化能力。

摘要：

依赖深度模型或物理模型是解决可编程照明计算显微术中逆样品重建问题的两种主流方法。基于物理模型的解决方案具有较强的泛化能力，但由于缺乏充分的物理约束，难以对逆问题进行全局优化。相比之下，深度学习方法具有很强的解题能力，但由于物理原理不明确，其泛化能力常受质疑。此外，传统深度模型由于难以获取高质量训练数据且跨场景泛化能力有限，难以应用于某些特定场景。本文为结合深度模型与物理模型的优势，提出了一种包含三个子神经网络（两个深度学习网络和一个基于物理的网络）的混合框架。我们首先通过轻量级深度学习神经网络获得具有丰富语义信息的结果，然后将其作为物理网络的初始值，使其输出符合物理过程约束。随后，这两个结果被用作融合深度学习神经网络的输入，该网络利用两种不同模型重建结果之间的成对特征进一步提升成像质量。最终结果整合了深度模型与物理模型的优势，能够快速解决可编程照明计算显微术中的计算重建逆问题并取得更优结果。我们通过理论分析以及在分辨率靶标和生物样本上的实际实验，验证了所提混合框架的可行性与有效性。

英文摘要：

Relying on either deep models or physical models are two mainstream approaches for solving inverse sample reconstruction problems in programmable illumination computational microscopy. Solutions based on physical models possess strong generalization capabilities while struggling with global optimization of inverse problems due to a lack of insufficient physical constraints. In contrast, deep learning methods have strong problem-solving abilities, but their generalization ability is often questioned because of the unclear physical principles. Besides, conventional deep models are difficult to apply to some specific scenes because of the difficulty in acquiring high-quality training data and their limited capacity to generalize across different scenarios. In this paper, to combine the advantages of deep models and physical models together, we propose a hybrid framework consisting of three sub-neural networks (two deep learning networks and one physics-based network). We first obtain a result with rich semantic information through a light deep learning neural network and then use it as the initial value of the physical network to make its output comply with physical process constraints. These two results are then used as the input of a fusion deep learning neural work which utilizes the paired features between the reconstruction results of two different models to further enhance imaging quality. The final result integrates the advantages of both deep models and physical models and can quickly solve the computational reconstruction inverse problem in programmable illumination computational microscopy and achieve better results. We verified the feasibility and effectiveness of the proposed hybrid framework with theoretical analysis and actual experiments on resolution targets and biological samples.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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