论文标题
估计3D显微镜生物图像中的光差
Estimation of Optical Aberrations in 3D Microscopic Bioimages
论文作者
论文摘要
显微镜图像的质量通常患有光学畸变。这些畸变及其相关点的扩散功能必须进行定量估计以恢复畸变的图像。基于卷积神经网络的最新最先进的方法可以准确量化畸变，但仅限于点光源的图像，例如荧光珠。在这项研究中，我们描述了Phasenet的扩展，使其在生物样品的3D图像上使用。为此，我们的方法将特定于对象的信息结合到用于培训网络的模拟图像中。此外，我们通过Richardson-Lucy Deonvolution添加了基于Python的图像恢复。我们证明，具有预测的PSF的反卷积不仅可以消除模拟畸变，而且还可以提高带有未知残留PSF的真实原始显微镜图像的质量。我们提供代码，以快速，方便的预测和纠正畸变。
The quality of microscopy images often suffers from optical aberrations. These aberrations and their associated point spread functions have to be quantitatively estimated to restore aberrated images. The recent state-of-the-art method PhaseNet, based on a convolutional neural network, can quantify aberrations accurately but is limited to images of point light sources, e.g. fluorescent beads. In this research, we describe an extension of PhaseNet enabling its use on 3D images of biological samples. To this end, our method incorporates object-specific information into the simulated images used for training the network. Further, we add a Python-based restoration of images via Richardson-Lucy deconvolution. We demonstrate that the deconvolution with the predicted PSF can not only remove the simulated aberrations but also improve the quality of the real raw microscopic images with unknown residual PSF. We provide code for fast and convenient prediction and correction of aberrations.
