When using fluorescent microscopy to study cellular dynamics, trade-offs typically have to be made between light exposure and quality of recorded image to balance the phototoxicity and image signal-to-noise ratio. Image denoising is an important tool for retrieving information from dim cell images. Recently, deep learning based image denoising is becoming the leading method because of its promising denoising performance, achieved by leveraging available prior knowledge about the noise model and samples at hand. We demonstrate that incorporating temporal information in the model can further improve the results. However, the practical application of this method has seen challenges because of the requirement of large, task-specific training datasets. In this work, we addressed this challenge by combining self-supervised learning with transfer learning, which eliminated the demand of task-matched training data while maintaining denoising performance. We demonstrate its application in fluorescent imaging of different subcellular structures.

中文翻译：

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通过监督到自监督转移学习的荧光显微镜图像去噪

当使用荧光显微镜研究细胞动力学时，通常必须在曝光和记录图像质量之间进行权衡，以平衡光毒性和图像信噪比。图像去噪是从昏暗的细胞图像中检索信息的重要工具。最近，基于深度学习的图像去噪正成为领先的方法，因为它具有良好的去噪性能，通过利用有关噪声模型和手头样本的现有先验知识来实现​​。我们证明在模型中加入时间信息可以进一步改善结果。然而，由于需要大型的、特定于任务的训练数据集，这种方法的实际应用遇到了挑战。在这项工作中，我们通过将自监督学习与转移学习相结合来解决这一挑战，这消除了对任务匹配训练数据的需求，同时保持去噪性能。我们展示了它在不同亚细胞结构的荧光成像中的应用。