傅里叶圆环相关技术简化了荧光显微镜的图像恢复。
Fourier ring correlation simplifies image restoration in fluorescence microscopy.
机构信息
Molecular Microscopy and Spectroscopy, Istituto Italiano di Tecnologia, Genoa, 16152, Italy.
Department of Cell Biology and Anatomy, Laboratory of Biophysics, Institute of Biomedicine and Medicity Research Laboratories, University of Turku, Turku, 20520, Finland.
出版信息
Nat Commun. 2019 Jul 15;10(1):3103. doi: 10.1038/s41467-019-11024-z.
Fourier ring correlation (FRC) has recently gained popularity among fluorescence microscopists as a straightforward and objective method to measure the effective image resolution. While the knowledge of the numeric resolution value is helpful in e.g., interpreting imaging results, much more practical use can be made of FRC analysis-in this article we propose blind image restoration methods enabled by it. We apply FRC to perform image de-noising by frequency domain filtering. We propose novel blind linear and non-linear image deconvolution methods that use FRC to estimate the effective point-spread-function, directly from the images. We show how FRC can be used as a powerful metric to observe the progress of iterative deconvolution. We also address two important limitations in FRC that may be of more general interest: how to make FRC work with single images (within certain practical limits) and with three-dimensional images with highly anisotropic resolution.
傅里叶环相关(FRC)最近在荧光显微镜领域受到欢迎,成为一种测量有效图像分辨率的简单而客观的方法。虽然了解数字分辨率值有助于解释成像结果,但 FRC 分析的实际用途更多——本文提出了基于它的盲图像恢复方法。我们应用 FRC 通过频域滤波进行图像去噪。我们提出了新颖的盲线性和非线性图像反卷积方法,它们使用 FRC 直接从图像中估计有效点扩散函数。我们展示了如何将 FRC 用作强大的指标来观察迭代反卷积的进展。我们还解决了 FRC 中两个可能更普遍感兴趣的重要限制:如何使 FRC 适用于单张图像(在某些实际限制内)和具有高度各向异性分辨率的三维图像。