Combination of Fast, Robust TV-based Deconvolution with Variational Segmentation for Microscopic Data

使用显微镜进行生物学定量测量可以了解细胞功能。必要的基础是在低光子计数情况下这种测量的可靠性和灵敏度，因为需要低光子计数以避免光漂白或细胞损伤（光毒性）。在这个项目中，我们将为荧光显微镜实施这样一个强大的测量系统。它将首次结合 PSF 估计、分割和反卷积的最佳方法。我们将开发一种使用 Zernike 多项式进行 PSF 估计的方法，该多项式适用于物理上可行的 PSF 的自由度以及系统的物理噪声模型。我们通过使用组合的分割-盲反卷积方法来扩展该方法，将 PSF 估计限制在我们分割已知结构属性的对象的区域。这种组合不仅会带来更好的结果，我们还将克服盲反卷积的已知局限性，即对 PSF 核的不敏感性。为了使该系统在实践中适用，实施了加速计算的新方法，例如使用管方法、自适应恢复和有序子集。与现有技术相比，我们预计准确性和可靠性将得到显着提高。

Quantitative measurements in biology using microscopy allow understanding of cellfunctions. The necessary basis is the reliability and sensitivity of such a measurement in the case of low photon counts since a low photon count is required in order to avoid photo bleaching or damages to the cell (phototoxity). In this project, we will implement such a robust measurement system for fluorescent microscopy. It will for the first time combine optimal methods for PSF estimation, segmentation, and deconvolution. We will develop a method for PSF estimation using Zernike polynomials that are adapted to both, the degrees of freedom of physically feasible PSFs, and the physical noise model of the system. We extend this method by using a combined segmentation-blind deconvolution approach, restricting the PSF estimation to areas where we segment objects of known structure properties. This combination should not only lead to better results, we will also overcome known limitations of blind deconvolution, i.e. the insensitivity to the PSF kernel. To make the system applicable in practice, new approaches to speed up computations are implemented, e.g. using tube methods, adaptive restoration and ordered subsets. We expect a significant improvement in accuracy and reliability compared to the state of the art.