Abstract
The use of fluorescent molecules to create long sequences of low-density, diffraction-limited images enables highly-precise molecule localization. However, this methodology requires lengthy imaging times, which limits the ability to view dynamic interactions of live cells on short time scales. Many techniques have been developed to reduce the number of frames needed for localization, from classic iterative optimization to deep neural networks. Particularly, deep algorithm unrolling utilizes both the structure of iterative sparse recovery algorithms and the performance gains of supervised deep learning. However, the robustness of this approach is highly dependant on having sufficient training data. In this paper we introduce deep unrolled self-supervised learning, which alleviates the need for such data by training a sequence-specific, model-based autoencoder that learns only from given measurements. Our proposed method exceeds the performance of its supervised counterparts, thus allowing for robust, dynamic imaging well below the diffraction limit without any labeled training samples. Furthermore, the suggested model-based autoencoder scheme can be utilized to enhance generalization in any sparse recovery framework, without the need for external training data.
Abstract (translated)
使用荧光分子创建长序列低密度、扩散限制的图像，使得对分子进行高精度定位。然而，这种方法需要较长的成像时间，这限制了在短时间尺度上观察活细胞动态相互作用的能力。为了减少对定位所需帧数的方法已经开发了许多，从经典的迭代优化到深度神经网络。特别，深度算法扩展利用了迭代稀疏恢复算法的结构和监督深度学习的性能提升。然而，这种方法的成功与否高度依赖于是否有足够的训练数据。在本文中，我们引入了深度自旋卷积学习，通过训练一个仅从给定测量中学习的序列特定的基于模型的自编码器来减轻这种依赖。与监督方法相比，我们所提出的方法超越了其性能，从而允许在衍射极限以下进行稳健、动态成像，而无需任何标记训练样本。此外，所提出的模型基于自编码器方案可以在任何稀疏恢复框架中增强泛化，而无需外部训练数据。
URL
https://arxiv.org/abs/2403.16974
