论文标题
使用数据驱动的畸变建模的无校准定量相成像
Calibration-free quantitative phase imaging using data-driven aberration modeling
论文作者
论文摘要
我们提出了一种数据驱动的方法，以补偿无校准定量相成像（QPI）中的光学畸变。与需要其他测量值或背景区域以纠正畸变的现有方法不同，我们利用深度学习技术来对成像系统中的像差物理学进行建模。我们通过使用基于U-NET的深神经网络来证明单发畸变校正的场图像的产生，该网络在具有畸变的光场和被畸变校正的场之间学习了翻译。在各种汇合真核细胞的2D和3D QPI测量上证明了我们方法的高保真度，使用背景减法对常规方法进行基准测试。
We present a data-driven approach to compensate for optical aberration in calibration-free quantitative phase imaging (QPI). Unlike existing methods that require additional measurements or a background region to correct aberrations, we exploit deep learning techniques to model the physics of aberration in an imaging system. We demonstrate the generation of a single-shot aberration-corrected field image by using a U-net-based deep neural network that learns a translation between an optical field with aberrations and an aberration-corrected field. The high fidelity of our method is demonstrated on 2D and 3D QPI measurements of various confluent eukaryotic cells, benchmarking against the conventional method using background subtractions.
