Dual-wavelength in-line digital holography with untrained deep neural networks
Dual-wavelength in-line digital holography with untrained deep neural networks
复制标题
具有未经训练的深度神经网络的双波长在线数字全息术
DOI：
10.1364/prj.441054
复制
发表时间：
2021-12-01
影响因子：
7.6
通讯作者：
Yao, Baoli
中科院分区：
文献类型：
--
作者：
Bai, Chen;Peng, Tong;Yao, Baoli
双波长同轴数字全息术（DIDH）是一种非接触、高精度的物体相位定量成像方法。在这些对象的重建中的两个技术挑战包括抑制放大的噪声和孪生图像，其分别源自相位差和相位共轭波前。与传统方法相比，深度学习网络已成为在DIDH中估计相位信息的强大工具，并具有噪声抑制或孪生图像去除能力。然而，目前大多数基于深度学习的方法都依赖于监督学习和训练实例，因此在将这种训练应用于实际成像设置时存在弱点。本文提出了一种新的DIDH网络（DIDH-Net），它将先验图像信息和物理成像过程封装在未经训练的深度神经网络中。DIDH-Net通过自动调整网络权值，可以有效地同时抑制DIDH的放大噪声和孪生图像。实验结果表明，该方法具有较强的相位重构能力，能够有效地提高DIDH的成像性能。(C)2021中国激光出版社
Dual-wavelength in-line digital holography (DIDH) is one of the popular methods for quantitative phase imaging of objects with non-contact and high-accuracy features. Two technical challenges in the reconstruction of these objects include suppressing the amplified noise and the twin-image that respectively originate from the phase difference and the phase-conjugated wavefronts. In contrast to the conventional methods, the deep learning network has become a powerful tool for estimating phase information in DIDH with the assistance of noise suppressing or twin-image removing ability. However, most of the current deep learning-based methods rely on supervised learning and training instances, thereby resulting in weakness when it comes to applying this training to practical imaging settings. In this paper, a new DIDH network (DIDH-Net) is proposed, which encapsulates the prior image information and the physical imaging process in an untrained deep neural network. The DIDH-Net can effectively suppress the amplified noise and the twin-image of the DIDH simultaneously by automatically adjusting the weights of the network. The obtained results demonstrate that the proposed method with robust phase reconstruction is well suited to improve the imaging performance of DIDH. (C) 2021 Chinese Laser Press