Coded-illumination can enable quantitative phase microscopy of transparent
samples with minimal hardware requirements. Intensity images are captured with
different source patterns and a non-linear phase retrieval optimization
reconstructs the image. The non-linear nature of the processing makes
optimizing the illumination pattern designs complicated. Traditional techniques
for experimental design (e.g. condition number optimization, spectral analysis)
consider only linear measurement formation models and linear reconstructions.
Deep neural networks (DNNs) can efficiently represent the non-linear process
and can be optimized over via training in an end-to-end framework. However,
DNNs typically require a large amount of training examples and parameters to
properly learn the phase retrieval process, without making use of the known
physical models. Here, we aim to use both our knowledge of the physics and the
power of machine learning together. We develop a new data-driven approach to
optimizing coded-illumination patterns for a LED array microscope for a given
phase reconstruction algorithm. Our method incorporates both the physics of the
measurement scheme and the non-linearity of the reconstruction algorithm into
the design problem. This enables efficient parameterization, which allows us to
use only a small number of training examples to learn designs that generalize
well in the experimental setting without retraining. We show experimental
results for both a well-characterized phase target and mouse fibroblast cells
using coded-illumination patterns optimized for a sparsity-based phase
reconstruction algorithm. Our learned design results using 2 measurements
demonstrate similar accuracy to Fourier Ptychography with 69 measurements.

本文提出了一种新的数据驱动方法，通过机器学习技术和物理学知识相结合，优化了 LED 阵列显微镜的编码照明模式，并将其应用于稀疏性相位重建算法，证明了该方法可以显著提高实验结果的准确性，且只需较少的训练数据。