Pitkäaho Tomi, Manninen Aki, Naughton Thomas J
Appl Opt. 2019 Feb 10;58(5):A202-A208. doi: 10.1364/AO.58.00A202.
Deep artificial neural network learning is an emerging tool in image analysis. We demonstrate its potential in the field of digital holographic microscopy by addressing the challenging problem of determining the in-focus reconstruction depth of Madin-Darby canine kidney cell clusters encoded in digital holograms. A deep convolutional neural network learns the in-focus depths from half a million hologram amplitude images. The trained network correctly determines the in-focus depth of new holograms with high probability, without performing numerical propagation. This paper reports on extensions to preliminary work published earlier as one of the first applications of deep learning in the field of digital holographic microscopy.
深度人工神经网络学习是图像分析中的一种新兴工具。我们通过解决确定数字全息图中编码的马-达二氏犬肾细胞簇的焦内重建深度这一具有挑战性的问题，展示了其在数字全息显微镜领域的潜力。一个深度卷积神经网络从50万个全息图振幅图像中学习焦内深度。经过训练的网络能够以高概率正确确定新全息图的焦内深度，而无需进行数值传播。本文报告了对早期发表的初步工作的扩展，这是深度学习在数字全息显微镜领域的首批应用之一。
