Choi Gunho, Ryu DongHun, Jo YoungJu, Kim Young Seo, Park Weisun, Min Hyun-Seok, Park YongKeun
Opt Express. 2019 Feb 18;27(4):4927-4943. doi: 10.1364/OE.27.004927.
We present a deep neural network to reduce coherent noise in three-dimensional quantitative phase imaging. Inspired by the cycle generative adversarial network, the denoising network was trained to learn a transform between two image domains: clean and noisy refractive index tomograms. The unique feature of this network, distinct from previous machine learning approaches employed in the optical imaging problem, is that it uses unpaired images. The learned network quantitatively demonstrated its performance and generalization capability through denoising experiments of various samples. We concluded by applying our technique to reduce the temporally changing noise emerging from focal drift in time-lapse imaging of biological cells. This reduction cannot be performed using other optical methods for denoising.
我们提出了一种深度神经网络，以减少三维定量相位成像中的相干噪声。受循环生成对抗网络的启发，去噪网络经过训练，用于学习两个图像域之间的变换：干净的和有噪声的折射率断层图像。该网络的独特之处在于，与之前用于光学成像问题的机器学习方法不同，它使用的是未配对图像。通过对各种样本的去噪实验，所学习的网络定量地展示了其性能和泛化能力。我们通过应用我们的技术来减少生物细胞延时成像中由焦点漂移产生的随时间变化的噪声，得出了结论。这种减少无法使用其他光学去噪方法来实现。
